Ying Ling, Fuchuan Tang, Xin Li, Dong Bin, Chunyan Yang
This paper proposes a high-confrontation botnet theoretical model from the attacker's point of view, which is based on the terminal-aware strategy, improves the network's anti-analysis, anti-pollution, and anti-infiltration capabilities, and based on this, further enhances the network's robustness and destructive resistance through the self-organization and reconstruction mechanism. It is of great practical significance to discuss its possible defense strategies and propose effective defense measures before attackers for this kind of potential new highly adversarial botnets.
{"title":"Research on intelligent botnet defense and analysis technology based on dynamic adversarial models","authors":"Ying Ling, Fuchuan Tang, Xin Li, Dong Bin, Chunyan Yang","doi":"10.1117/12.3031979","DOIUrl":"https://doi.org/10.1117/12.3031979","url":null,"abstract":"This paper proposes a high-confrontation botnet theoretical model from the attacker's point of view, which is based on the terminal-aware strategy, improves the network's anti-analysis, anti-pollution, and anti-infiltration capabilities, and based on this, further enhances the network's robustness and destructive resistance through the self-organization and reconstruction mechanism. It is of great practical significance to discuss its possible defense strategies and propose effective defense measures before attackers for this kind of potential new highly adversarial botnets.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the complex electromagnetic environment of the 230-270MHz ultra short wave frequency band, traditional energy detection methods suffer from missed detections and high false alarm rates in broadband satellite signals. This paper proposes a broadband ultra short wave signal detection method based on the Short Cut Swin Transformer YOLOV5s (SST-YOLOV5s) network with spectrum superposition, Effectively addressing the challenge of detecting broadband satellite channels in low signal-to-noise ratio scenarios, a problem often encountered with traditional methods. Additionally, tackling the issue of elevated false alarm rates when interference anomalies are present. Firstly, by overlaying spectra, the discrimination between ultra short wave signals and bottom noise is highlighted, and the influence of short burst interference is suppressed, Enhancing the target signal characteristics effectively amidst a low signal-to-noise ratio. Simultaneously, a four layer SC (shortcut)-ST (Swin Transformer) and multi-layer convolutional cascaded ultra short wave signal feature extraction backbone network SST-Backbone (SC-ST-Backbone) are proposed. In the backbone network, the SC-ST module utilizes the global attention to global features of the Transformer, combined with residual multi-layer convolution modules that focus on local features, to increase the depth and receptive field of the network, making the network model more accurate in reconnaissance and detection of broadband ultra short wave signals in the target frequency band. It can efficiently remove the interference of bottom noise features and reduce the attention to abnormal signal features, Improved the detection accuracy of broadband ultra short wave target signals in complex environments and reduced false alarm rates.
{"title":"Detection of ultrashort wave broadband satellite signal based on overlay spectrum and SST YOLOV5s","authors":"Shoubin Wang, Xianwu Sha, Shang Wu, Lei Shen","doi":"10.1117/12.3031909","DOIUrl":"https://doi.org/10.1117/12.3031909","url":null,"abstract":"In the complex electromagnetic environment of the 230-270MHz ultra short wave frequency band, traditional energy detection methods suffer from missed detections and high false alarm rates in broadband satellite signals. This paper proposes a broadband ultra short wave signal detection method based on the Short Cut Swin Transformer YOLOV5s (SST-YOLOV5s) network with spectrum superposition, Effectively addressing the challenge of detecting broadband satellite channels in low signal-to-noise ratio scenarios, a problem often encountered with traditional methods. Additionally, tackling the issue of elevated false alarm rates when interference anomalies are present. Firstly, by overlaying spectra, the discrimination between ultra short wave signals and bottom noise is highlighted, and the influence of short burst interference is suppressed, Enhancing the target signal characteristics effectively amidst a low signal-to-noise ratio. Simultaneously, a four layer SC (shortcut)-ST (Swin Transformer) and multi-layer convolutional cascaded ultra short wave signal feature extraction backbone network SST-Backbone (SC-ST-Backbone) are proposed. In the backbone network, the SC-ST module utilizes the global attention to global features of the Transformer, combined with residual multi-layer convolution modules that focus on local features, to increase the depth and receptive field of the network, making the network model more accurate in reconnaissance and detection of broadband ultra short wave signals in the target frequency band. It can efficiently remove the interference of bottom noise features and reduce the attention to abnormal signal features, Improved the detection accuracy of broadband ultra short wave target signals in complex environments and reduced false alarm rates.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we focus on the online 3D bin packing problem, a classical strong NP-hard problem. In the problem, each item is unknown before bin packing is performed, and the arrival of the item requires immediate bin packing, which has many applications in industrial automation. In this paper, we propose a greedy algorithm for multi-indicator fusion to solve this problem by defining a series of evaluation indicators during bin packing, determining the weights of these indicators to be fused by SVR algorithm and Quasi-Newton Methods, and finally selecting the placement with the highest score of the fused indicators to be placed. The experimental results show that this method can solve the online 3D bin packing problem and is competitive with other algorithms in terms of space utilization and the number of bins, and the running time is fully completed to meet the online bin packing requirements.
在本文中，我们将重点研究在线 3D 仓储包装问题，这是一个经典的强 NP 难问题。在该问题中，每个物品在装箱前都是未知的，物品到达后需要立即装箱，这在工业自动化领域有很多应用。本文提出了一种多指标融合的贪婪算法来解决这一问题，即在料仓打包过程中定义一系列评价指标，通过 SVR 算法和准牛顿方法确定这些指标的权重进行融合，最后选择融合后指标得分最高的放置点进行放置。实验结果表明，该方法可以解决在线三维料仓打包问题，在空间利用率和料仓数量方面与其他算法相比具有竞争力，运行时间完全可以满足在线料仓打包的要求。
{"title":"A greedy online 3D bin packing algorithm based on multi-indicator fusion","authors":"Lixin Ma, Wei Wang, Tong Zhang, Xincheng Tian, Yong Jiang","doi":"10.1117/12.3031910","DOIUrl":"https://doi.org/10.1117/12.3031910","url":null,"abstract":"In this paper, we focus on the online 3D bin packing problem, a classical strong NP-hard problem. In the problem, each item is unknown before bin packing is performed, and the arrival of the item requires immediate bin packing, which has many applications in industrial automation. In this paper, we propose a greedy algorithm for multi-indicator fusion to solve this problem by defining a series of evaluation indicators during bin packing, determining the weights of these indicators to be fused by SVR algorithm and Quasi-Newton Methods, and finally selecting the placement with the highest score of the fused indicators to be placed. The experimental results show that this method can solve the online 3D bin packing problem and is competitive with other algorithms in terms of space utilization and the number of bins, and the running time is fully completed to meet the online bin packing requirements.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate identification of Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions can enhance the precision of indoor positioning. This paper proposes a method for identifying LOS and NLOS channel states in millimeter-wave indoor wireless positioning based on machine learning. In this approach, we introduce angular and frequency domain features for the first time and combine them with traditional channel characteristics to improve the accuracy of millimeter-wave indoor LOS/NLOS scene classification. The method utilizes an artificial neural network to analyze five distinct channel indicators extracted from the spatial, temporal, and frequency domains: the angular difference of the strongest path, maximum received power, average excess delay, root mean square delay spread, and the kurtosis of the frequency domain transfer function. Simulation results show that this method achieves an accuracy rate of 97.58%.
准确识别视距（LOS）和非视距（NLOS）条件可以提高室内定位的精度。本文提出了一种基于机器学习的毫米波室内无线定位 LOS 和 NLOS 信道状态识别方法。在这种方法中，我们首次引入了角域和频域特征，并将其与传统信道特征相结合，以提高毫米波室内 LOS/NLOS 场景分类的准确性。该方法利用人工神经网络来分析从空间、时间和频率域提取的五个不同信道指标：最强路径的角差、最大接收功率、平均过量延迟、均方根延迟扩散和频域传递函数的峰度。仿真结果表明，这种方法的准确率达到了 97.58%。
{"title":"Machine learning-based classification method for millimeter wave indoor channel at 28 GHz","authors":"Youqiang Xu, Rongchen Sun","doi":"10.1117/12.3031962","DOIUrl":"https://doi.org/10.1117/12.3031962","url":null,"abstract":"Accurate identification of Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions can enhance the precision of indoor positioning. This paper proposes a method for identifying LOS and NLOS channel states in millimeter-wave indoor wireless positioning based on machine learning. In this approach, we introduce angular and frequency domain features for the first time and combine them with traditional channel characteristics to improve the accuracy of millimeter-wave indoor LOS/NLOS scene classification. The method utilizes an artificial neural network to analyze five distinct channel indicators extracted from the spatial, temporal, and frequency domains: the angular difference of the strongest path, maximum received power, average excess delay, root mean square delay spread, and the kurtosis of the frequency domain transfer function. Simulation results show that this method achieves an accuracy rate of 97.58%.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The traditional Modbus communication architecture usually consists of a single master station and multiple slave stations, which can lead to decreased communication efficiency in certain application scenarios. As IIoT (Industrial Internet of Things) continues to progress, there is a growing demand for new sophisticated applications that necessitate retrieving data from various industrial settings. As a result, multi-master station technology has been developed, enabling the retrieval of on-site data without disrupting the data collection process of the primary master station. However, most of these solutions require modifications to the original bus and the suspension of the original data collection process during installation. In order to maintain the integrity of the original bus system, this study introduces a Modbus multi-master technology in which the additional master employs a non-invasive listening approach to receive messages. It identifies request and response messages based on the protocol’s function codes and message byte numbers, parses information, and shares the acquired data to IIoT applications. The technology was tested in an IIoT application in which a WirelessHART network node was converted into such a master, which uploads acquired data wirelessly to the IIoT application. The findings indicate that the new master successfully identified messages and exchanged data with precision
{"title":"Protocol-based non-invasive Modbus monitoring device for industrial internet of things data sharing","authors":"Xuanzhi Huang, Deji Chen, Hongyuan Hu","doi":"10.1117/12.3032042","DOIUrl":"https://doi.org/10.1117/12.3032042","url":null,"abstract":"The traditional Modbus communication architecture usually consists of a single master station and multiple slave stations, which can lead to decreased communication efficiency in certain application scenarios. As IIoT (Industrial Internet of Things) continues to progress, there is a growing demand for new sophisticated applications that necessitate retrieving data from various industrial settings. As a result, multi-master station technology has been developed, enabling the retrieval of on-site data without disrupting the data collection process of the primary master station. However, most of these solutions require modifications to the original bus and the suspension of the original data collection process during installation. In order to maintain the integrity of the original bus system, this study introduces a Modbus multi-master technology in which the additional master employs a non-invasive listening approach to receive messages. It identifies request and response messages based on the protocol’s function codes and message byte numbers, parses information, and shares the acquired data to IIoT applications. The technology was tested in an IIoT application in which a WirelessHART network node was converted into such a master, which uploads acquired data wirelessly to the IIoT application. The findings indicate that the new master successfully identified messages and exchanged data with precision","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pediatric diseases are challenging to diagnose due to their complex and diverse characteristics. To assist doctors in diagnosis and help them make informed decisions, this paper proposes a Knowledge graph and Large language model Knowledge-Enhanced (KLKE) intelligent diagnosis model. The intelligent diagnosis task is treated as a text classification task, where the original Electronic Medical Record are input into MacBERT model encoder to obtain the contextual representation after key information enhancement and KG prompted LLM enhancement respectively. The final text representation is obtained by concatenating and merging the enhanced representations. Graph Convolutional Network is utilized to obtain the knowledge representation and the two representations are fused using a fusion method based on interactive attention mechanism. Experiments are conducted on PeEMR, and compared with models that only fuses triples and graph structures. The KLKE achieved an increase of 9.15% and 2.28% in F1_micro scores respectively.
{"title":"Research on KG and LLM knowledge-enhanced pediatric diseases intelligent diagnosis","authors":"Wenhui Fu, Dongming Dai, Kunli Zhang, Xiaomei Liu, Heng Zhang, Lingxiang Ao, Jinlong Xiao","doi":"10.1117/12.3032061","DOIUrl":"https://doi.org/10.1117/12.3032061","url":null,"abstract":"Pediatric diseases are challenging to diagnose due to their complex and diverse characteristics. To assist doctors in diagnosis and help them make informed decisions, this paper proposes a Knowledge graph and Large language model Knowledge-Enhanced (KLKE) intelligent diagnosis model. The intelligent diagnosis task is treated as a text classification task, where the original Electronic Medical Record are input into MacBERT model encoder to obtain the contextual representation after key information enhancement and KG prompted LLM enhancement respectively. The final text representation is obtained by concatenating and merging the enhanced representations. Graph Convolutional Network is utilized to obtain the knowledge representation and the two representations are fused using a fusion method based on interactive attention mechanism. Experiments are conducted on PeEMR, and compared with models that only fuses triples and graph structures. The KLKE achieved an increase of 9.15% and 2.28% in F1_micro scores respectively.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep Q learning is a crucial method of deep reinforcement learning and has achieved remarkable success in multiple applications. However, Deep Q-learning suffers from low sample efficiency. To overcome this limitation, we introduce a novel algorithm, adaptive prediction sample network (APSN), to improve the sample efficiency. APSN is designed to predict the importance of each sample to policy updates, enabling efficient sample selection. We introduce a new metric to evaluate the importance of samples and use it to train the APSN network. In the experimental parts, we evaluate our algorithm on four Atari games in OpenAI Gym and compare APSN with SDQN. Experimental results show that APSN performs better in terms of sample efficiency and provides an effective solution for improving the sample efficiency of deep reinforcement learning. This research result is expected to promote the performance of deep reinforcement learning algorithms in practical applications.
{"title":"APSN: adaptive prediction sample network in Deep Q learning","authors":"Shijie Chu","doi":"10.1117/12.3031933","DOIUrl":"https://doi.org/10.1117/12.3031933","url":null,"abstract":"Deep Q learning is a crucial method of deep reinforcement learning and has achieved remarkable success in multiple applications. However, Deep Q-learning suffers from low sample efficiency. To overcome this limitation, we introduce a novel algorithm, adaptive prediction sample network (APSN), to improve the sample efficiency. APSN is designed to predict the importance of each sample to policy updates, enabling efficient sample selection. We introduce a new metric to evaluate the importance of samples and use it to train the APSN network. In the experimental parts, we evaluate our algorithm on four Atari games in OpenAI Gym and compare APSN with SDQN. Experimental results show that APSN performs better in terms of sample efficiency and provides an effective solution for improving the sample efficiency of deep reinforcement learning. This research result is expected to promote the performance of deep reinforcement learning algorithms in practical applications.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problems of punctuality, parking accuracy, energy saving and comfort in the automatic driving of urban rail trains, this paper proposes an algorithm for generating planned speed profile based on improved genetic algorithm. This improved genetic algorithm aims to achieve multi-objective optimization of on-time, accurate parking, energy saving and comfort and improve the optimization efficiency of traditional genetic algorithms. The simulation results show that the proposed algorithm can satisfy the basic constraints of safe, punctual and accurate stopping of trains. The algorithm also reduces the operation energy consumption and improves the operation comfort.
{"title":"Research on speed profile generation of train automatic driving planning based on improved genetic algorithm","authors":"Qinyue Zhu, Runkai Hua, Yichen Yu, Jiyuan Li","doi":"10.1117/12.3032033","DOIUrl":"https://doi.org/10.1117/12.3032033","url":null,"abstract":"Aiming at the problems of punctuality, parking accuracy, energy saving and comfort in the automatic driving of urban rail trains, this paper proposes an algorithm for generating planned speed profile based on improved genetic algorithm. This improved genetic algorithm aims to achieve multi-objective optimization of on-time, accurate parking, energy saving and comfort and improve the optimization efficiency of traditional genetic algorithms. The simulation results show that the proposed algorithm can satisfy the basic constraints of safe, punctual and accurate stopping of trains. The algorithm also reduces the operation energy consumption and improves the operation comfort.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chang Wang, Zhiqiong Liu, Jin Liu, Wang Li, Junxin Chen
FaaS enables users to focus on developing function codes rather than managing complex infrastructure, as the serverless computing platform takes responsibility for resource management and dynamically scales computing resources for serverless functions. While serverless computing platform provides efficient hardware resource management and provisioning, they suffer from weaker computing performance due to the latency associated with serverless function startup. Startup latency refers to the time required to prepare execution environments for user functions. To alleviate this latency, this paper proposes a container scheduling policy aimed at reducing startup latency by reducing the likelihood of container cold starts. This is achieved by unifying language runtime images, creating pre-warm container pools, and warm containers. We formulate the startup latency problem and implement a scheduling policy in a serverless computing platform. Simulation results demonstrate that our proposed scheduling policy effectively reduces overall startup latency while ensuring optimal computing performance for user functions.
{"title":"Towards a container scheduling policy for alleviating total startup latency in serverless computing platform","authors":"Chang Wang, Zhiqiong Liu, Jin Liu, Wang Li, Junxin Chen","doi":"10.1117/12.3032003","DOIUrl":"https://doi.org/10.1117/12.3032003","url":null,"abstract":"FaaS enables users to focus on developing function codes rather than managing complex infrastructure, as the serverless computing platform takes responsibility for resource management and dynamically scales computing resources for serverless functions. While serverless computing platform provides efficient hardware resource management and provisioning, they suffer from weaker computing performance due to the latency associated with serverless function startup. Startup latency refers to the time required to prepare execution environments for user functions. To alleviate this latency, this paper proposes a container scheduling policy aimed at reducing startup latency by reducing the likelihood of container cold starts. This is achieved by unifying language runtime images, creating pre-warm container pools, and warm containers. We formulate the startup latency problem and implement a scheduling policy in a serverless computing platform. Simulation results demonstrate that our proposed scheduling policy effectively reduces overall startup latency while ensuring optimal computing performance for user functions.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
By utilizing geometric and astronomical knowledge, a model for the length of a solar shadow in relation to its geographical location and object height is established. The variations of shadow length concerning various parameters are analyzed. The model incorporates geographical latitude, longitude, day of the year, time, etc., to calculate the solar altitude angle and, in conjunction with object height, establishes a model for calculating object projection length. Finally, using the provided data in the appendix, the curve of solar shadow length variation at a given time is obtained.
{"title":"Analysis of a model algorithm for calculating object projection length","authors":"Bing Yang, Qiao Guo, Gaoyang Su, Zhiyuan Pan","doi":"10.1117/12.3031930","DOIUrl":"https://doi.org/10.1117/12.3031930","url":null,"abstract":"By utilizing geometric and astronomical knowledge, a model for the length of a solar shadow in relation to its geographical location and object height is established. The variations of shadow length concerning various parameters are analyzed. The model incorporates geographical latitude, longitude, day of the year, time, etc., to calculate the solar altitude angle and, in conjunction with object height, establishes a model for calculating object projection length. Finally, using the provided data in the appendix, the curve of solar shadow length variation at a given time is obtained.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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