Pub Date : 2024-04-15DOI: 10.17559/tv-20230719000815
Qunli Zhao, Hesheng Cheng, Chen Shen
: Mining the erasable itemset is an interesting research domain, which has been applied to solve the problem of how to efficiently use limited funds to optimise production in economic crisis. After the problem of mining the erasable itemset was posed, researchers have proposed many algorithms to solve it, among which mining the maximum erasable itemset is a significant direction for research. Since all subsets of the maximum erasable itemset are erasable itemsets, all erasable itemsets can be obtained by mining the maximum erasable itemset, which reduces both the quantity of candidate and resultant itemsets generated during the mining process. However, computing many itemset values still takes a lot of CPU time when mining huge amounts of data. And it is difficult to solve the problem quickly with sequential algorithms. Therefore, this proposed study presents a parallel algorithm for the mining of maximum erasable itemsets, called PAMMEI, based on a multi-core processor platform. The algorithm divides the entire mining task into multiple subtasks and assigns them to multiple processor cores for parallel execution, while using an efficient pruning strategy to downsize the space to be searched and increase the mining speed. To verify the efficiency of the PAMMEI algorithm, the paper compares it with most advanced algorithms. The experimental results show that PAMMEI is superior to the comparable algorithms with respect to runtime, memory usage and scalability.
:挖掘可擦除项集是一个有趣的研究领域，它被应用于解决经济危机下如何有效利用有限资金优化生产的问题。挖掘可擦除项集问题提出后，研究人员提出了许多算法来解决这个问题，其中挖掘最大可擦除项集是一个重要的研究方向。由于最大可擦除项集的所有子集都是可擦除项集，因此通过挖掘最大可擦除项集可以得到所有可擦除项集，这就减少了挖掘过程中产生的候选项集和结果项集的数量。然而，在挖掘海量数据时，计算许多项集值仍然需要耗费大量的 CPU 时间。而顺序算法很难快速解决这个问题。因此，本研究提出了一种基于多核处理器平台的最大可擦除项集挖掘并行算法，称为 PAMMEI。该算法将整个挖掘任务划分为多个子任务，并将其分配给多个处理器内核并行执行，同时采用高效的剪枝策略来缩小搜索空间，提高挖掘速度。为了验证 PAMMEI 算法的效率，本文将其与最先进的算法进行了比较。实验结果表明，PAMMEI 在运行时间、内存使用和可扩展性方面都优于同类算法。
{"title":"A Parallel Mining Algorithm for Maximum Erasable Itemset Based on Multi-core Processor","authors":"Qunli Zhao, Hesheng Cheng, Chen Shen","doi":"10.17559/tv-20230719000815","DOIUrl":"https://doi.org/10.17559/tv-20230719000815","url":null,"abstract":": Mining the erasable itemset is an interesting research domain, which has been applied to solve the problem of how to efficiently use limited funds to optimise production in economic crisis. After the problem of mining the erasable itemset was posed, researchers have proposed many algorithms to solve it, among which mining the maximum erasable itemset is a significant direction for research. Since all subsets of the maximum erasable itemset are erasable itemsets, all erasable itemsets can be obtained by mining the maximum erasable itemset, which reduces both the quantity of candidate and resultant itemsets generated during the mining process. However, computing many itemset values still takes a lot of CPU time when mining huge amounts of data. And it is difficult to solve the problem quickly with sequential algorithms. Therefore, this proposed study presents a parallel algorithm for the mining of maximum erasable itemsets, called PAMMEI, based on a multi-core processor platform. The algorithm divides the entire mining task into multiple subtasks and assigns them to multiple processor cores for parallel execution, while using an efficient pruning strategy to downsize the space to be searched and increase the mining speed. To verify the efficiency of the PAMMEI algorithm, the paper compares it with most advanced algorithms. The experimental results show that PAMMEI is superior to the comparable algorithms with respect to runtime, memory usage and scalability.","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"280 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140704100","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}
Pub Date : 2024-04-15DOI: 10.17559/tv-20230516000638
Zhiyan Zhang, Xianghui Guo, Pengju Yang, Taoyun Wang, Yuqi Ji, Lina Yao, Jinshan Power, Supply Company
: With the increasing integration of distributed photovoltaic (PV) generation into distribution networks, challenges such as power reverse flow and high line losses have emerged, leading to greater uncertainty in power systems. To address these issues, this paper presents an analytical model for calculating line losses in low-voltage distribution networks with PV generation, utilizing power flow calculations. A simulation model of a 15 node low-voltage network is developed using SIMULINK to validate the accuracy of the analytical model under the scenario of uniform load distribution (ULD). Additionally, a line loss optimization algorithm based on quantum genetic algorithms (QGA) is proposed for low-voltage distribution networks with distributed PV generation, along with an optimization model. The objective function of the optimization model is based on the reduction in line losses resulting from the integration of the PV system. The example results demonstrate the consistency between the line loss optimization using QGA and the analytical results, highlighting the significant advantages of QGA in terms of speed and accuracy. This research provides valuable insights for line loss optimization in low-voltage distribution networks with distributed PV generation and serves as a theoretical reference for future studies in this field.
{"title":"Line Loss Calculation and Optimization in Low Voltage Lines with Photovoltaic Systems Using an Analytical Model and Quantum Genetic Algorithm","authors":"Zhiyan Zhang, Xianghui Guo, Pengju Yang, Taoyun Wang, Yuqi Ji, Lina Yao, Jinshan Power, Supply Company","doi":"10.17559/tv-20230516000638","DOIUrl":"https://doi.org/10.17559/tv-20230516000638","url":null,"abstract":": With the increasing integration of distributed photovoltaic (PV) generation into distribution networks, challenges such as power reverse flow and high line losses have emerged, leading to greater uncertainty in power systems. To address these issues, this paper presents an analytical model for calculating line losses in low-voltage distribution networks with PV generation, utilizing power flow calculations. A simulation model of a 15 node low-voltage network is developed using SIMULINK to validate the accuracy of the analytical model under the scenario of uniform load distribution (ULD). Additionally, a line loss optimization algorithm based on quantum genetic algorithms (QGA) is proposed for low-voltage distribution networks with distributed PV generation, along with an optimization model. The objective function of the optimization model is based on the reduction in line losses resulting from the integration of the PV system. The example results demonstrate the consistency between the line loss optimization using QGA and the analytical results, highlighting the significant advantages of QGA in terms of speed and accuracy. This research provides valuable insights for line loss optimization in low-voltage distribution networks with distributed PV generation and serves as a theoretical reference for future studies in this field.","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"39 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702904","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}
Pub Date : 2024-04-15DOI: 10.17559/tv-20230628001154
Yuanchen Chai
: With the increasing influence of online public opinion, mining opinions and trend analysis from massive data of online media is important for understanding user sentiment, managing brand reputation, analyzing public opinion and optimizing marketing strategies. By combining data from multiple perceptual modalities, more comprehensive and accurate sentiment analysis results can be obtained. However, using multimodal data for sentiment analysis may face challenges such as data fusion, modal imbalance and inter-modal correlation. To overcome these challenges, the paper introduces an attention mechanism to multimodal sentiment analysis by constructing text, image, and audio feature extractors and using a custom cross-modal attention layer to compute the attention weights between different modalities, and finally fusing the attention-weighted features for sentiment classification. Through the cross-modal attention mechanism, the model can automatically learn the correlation between different modalities, dynamically adjust the modal weights, and selectively fuse features from different modalities, thus improving the accuracy and expressiveness of sentiment analysis.
{"title":"Emotion Intensity Detection in Online Media: An Attention Mechanism Based Multimodal Deep Learning Approach","authors":"Yuanchen Chai","doi":"10.17559/tv-20230628001154","DOIUrl":"https://doi.org/10.17559/tv-20230628001154","url":null,"abstract":": With the increasing influence of online public opinion, mining opinions and trend analysis from massive data of online media is important for understanding user sentiment, managing brand reputation, analyzing public opinion and optimizing marketing strategies. By combining data from multiple perceptual modalities, more comprehensive and accurate sentiment analysis results can be obtained. However, using multimodal data for sentiment analysis may face challenges such as data fusion, modal imbalance and inter-modal correlation. To overcome these challenges, the paper introduces an attention mechanism to multimodal sentiment analysis by constructing text, image, and audio feature extractors and using a custom cross-modal attention layer to compute the attention weights between different modalities, and finally fusing the attention-weighted features for sentiment classification. Through the cross-modal attention mechanism, the model can automatically learn the correlation between different modalities, dynamically adjust the modal weights, and selectively fuse features from different modalities, thus improving the accuracy and expressiveness of sentiment analysis.","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"21 S8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702244","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}
Pub Date : 2024-04-15DOI: 10.17559/tv-20230617000742
R. Jayamala, A. S. Oliver, J. Jayanthi
: Wireless Sensor Networks (WSN) track and record environmental changes using sensor nodes. When designing sensors, consider antenna type, components, memory, lifespan, security, computing power, communication protocol, energy consumption, etc. Wireless sensor networks (WSN) are ad hoc. This network links tiny sensor nodes that share few resources (both severely constrained at the node level). This paper proposed a Secure and Fast Real-Time (SFRT) Routing Protocol, which is used to secure real-time data transmissions in WSN. The proposed method not only increases the reliability of WSN but also offers a more robust solution in case a sensor node link fails. Discarding packets, launching a denial-of-service attack, using black holes, launching a selective forwarding attack, and flooding the network with hello packets are some proposed security measures. It maintains high packet throughput in the presence of malicious nodes while using little energy. Simulations have helped examine recommended safety measures. The unique approach outperformed state-of-the-art methods in the NS2 simulation in all relevant metrics, including network longevity, packet delivery rate, energy efficiency, network throughput, and end-to-end delivery latency. Most current methods necessitate multiple retransmissions before success is declared, increasing data transmission costs by 5% compared to the best approach. The proposed method is highlighted for its ability to increase network lifetime by 20% and reduce the total delay by 30%.
{"title":"Enhanced Secured and Real-Time Data Transmissions in Wireless Sensor Networks using SFRT Routing Protocol","authors":"R. Jayamala, A. S. Oliver, J. Jayanthi","doi":"10.17559/tv-20230617000742","DOIUrl":"https://doi.org/10.17559/tv-20230617000742","url":null,"abstract":": Wireless Sensor Networks (WSN) track and record environmental changes using sensor nodes. When designing sensors, consider antenna type, components, memory, lifespan, security, computing power, communication protocol, energy consumption, etc. Wireless sensor networks (WSN) are ad hoc. This network links tiny sensor nodes that share few resources (both severely constrained at the node level). This paper proposed a Secure and Fast Real-Time (SFRT) Routing Protocol, which is used to secure real-time data transmissions in WSN. The proposed method not only increases the reliability of WSN but also offers a more robust solution in case a sensor node link fails. Discarding packets, launching a denial-of-service attack, using black holes, launching a selective forwarding attack, and flooding the network with hello packets are some proposed security measures. It maintains high packet throughput in the presence of malicious nodes while using little energy. Simulations have helped examine recommended safety measures. The unique approach outperformed state-of-the-art methods in the NS2 simulation in all relevant metrics, including network longevity, packet delivery rate, energy efficiency, network throughput, and end-to-end delivery latency. Most current methods necessitate multiple retransmissions before success is declared, increasing data transmission costs by 5% compared to the best approach. The proposed method is highlighted for its ability to increase network lifetime by 20% and reduce the total delay by 30%.","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"42 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701821","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}
Pub Date : 2024-04-15DOI: 10.17559/tv-20230628000772
HU Feng
: This study employs science mapping and bibliometric analysis to chart the knowledge structure and research trajectory of enterprise green environment literature from 2002 to 2022. Despite rising interest, comprehensive analyses of this field's research landscapes and dynamics remain scarce. Through advanced techniques including discipline mapping, journal co-citation analysis, author co-citation analysis, and keyword co-occurrence analysis, this work elucidates the prominent disciplines, publications, authors, and research foci in enterprise of green environment scholarship over the past two decades. The results provide vital insights into the current status, influential leaders, core journals, knowledge gaps, and future directions of this rapidly evolving field. This science mapping analysis offers a valuable quantitative overview of green environment research enterprise that can inform scholars worldwide in producing impactful work on this critical area. The findings reveal profound implications for the developing structure and frontiers of sustainability-focused business and management research.
{"title":"Exploring the Landscape of Research on Enterprise Green Environments Through Science Mapping Analysis","authors":"HU Feng","doi":"10.17559/tv-20230628000772","DOIUrl":"https://doi.org/10.17559/tv-20230628000772","url":null,"abstract":": This study employs science mapping and bibliometric analysis to chart the knowledge structure and research trajectory of enterprise green environment literature from 2002 to 2022. Despite rising interest, comprehensive analyses of this field's research landscapes and dynamics remain scarce. Through advanced techniques including discipline mapping, journal co-citation analysis, author co-citation analysis, and keyword co-occurrence analysis, this work elucidates the prominent disciplines, publications, authors, and research foci in enterprise of green environment scholarship over the past two decades. The results provide vital insights into the current status, influential leaders, core journals, knowledge gaps, and future directions of this rapidly evolving field. This science mapping analysis offers a valuable quantitative overview of green environment research enterprise that can inform scholars worldwide in producing impactful work on this critical area. The findings reveal profound implications for the developing structure and frontiers of sustainability-focused business and management research.","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"60 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702516","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}
Pub Date : 2024-04-15DOI: 10.17559/tv-20230709000793
.
.
{"title":"EODM: On Developing Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN)","authors":"","doi":"10.17559/tv-20230709000793","DOIUrl":"https://doi.org/10.17559/tv-20230709000793","url":null,"abstract":".","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"307 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703671","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}
Pub Date : 2024-04-15DOI: 10.17559/tv-20230510000617
Xiangyu Li, Wei Hao, Meilin Xie, Bo Liu, Bo Jiang, LV Tao, Wei Song, Ping Ruan
: During non-landing measurements of a theodolite, the accuracy of the goniometric readings can be compromised by wobble errors induced by various factors such as wind loads, theodolite driving torque, and the stiffness of the supporting structure. To achieve high-precision non-landing measurements, it is essential to accurately determine and correct the platform wobble errors affecting the azimuth and pitch pointing angles. In this paper, a non-contact optical measurement method is proposed for quantifying platform wobble errors. The method establishes an auto-alignment optical path between an autocollimator and a reflector in the measuring device. By detecting the deviation angle of the CCD image point as the optical path changes, precise measurements of the platform wobble errors can be obtained. Experimental results demonstrate that the measuring device can achieve an auto-alignment optical path within 5 minutes, significantly improving measurement efficiency. Furthermore, after measuring the platform wobble error and applying data correction, the average error in the azimuth pointing angle is reduced from 31.5 ″ to 9.8 ″ , and the average error in the pitch pointing angle is reduced from 21 ″ to 9.2 ″ . These results highlight the substantial correction effect achieved by the proposed method.
{"title":"Auto-Alignment Non-Contact Optical Measurement Method for Quantifying Wobble Error of a Theodolite on a Vehicle-Mounted Platform","authors":"Xiangyu Li, Wei Hao, Meilin Xie, Bo Liu, Bo Jiang, LV Tao, Wei Song, Ping Ruan","doi":"10.17559/tv-20230510000617","DOIUrl":"https://doi.org/10.17559/tv-20230510000617","url":null,"abstract":": During non-landing measurements of a theodolite, the accuracy of the goniometric readings can be compromised by wobble errors induced by various factors such as wind loads, theodolite driving torque, and the stiffness of the supporting structure. To achieve high-precision non-landing measurements, it is essential to accurately determine and correct the platform wobble errors affecting the azimuth and pitch pointing angles. In this paper, a non-contact optical measurement method is proposed for quantifying platform wobble errors. The method establishes an auto-alignment optical path between an autocollimator and a reflector in the measuring device. By detecting the deviation angle of the CCD image point as the optical path changes, precise measurements of the platform wobble errors can be obtained. Experimental results demonstrate that the measuring device can achieve an auto-alignment optical path within 5 minutes, significantly improving measurement efficiency. Furthermore, after measuring the platform wobble error and applying data correction, the average error in the azimuth pointing angle is reduced from 31.5 ″ to 9.8 ″ , and the average error in the pitch pointing angle is reduced from 21 ″ to 9.2 ″ . These results highlight the substantial correction effect achieved by the proposed method.","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"314 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703607","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}
Pub Date : 2024-02-15DOI: 10.17559/tv-20230508000613
{"title":"Deployment with Location Knowledge by Multi Area Approach for Detecting Replica Nodes in Wireless Sensor Network","authors":"","doi":"10.17559/tv-20230508000613","DOIUrl":"https://doi.org/10.17559/tv-20230508000613","url":null,"abstract":"","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"8 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139774111","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}
Pub Date : 2024-02-15DOI: 10.17559/tv-20230328000484
{"title":"Managing the Human Potential of Highly Educated Experts in the Field of Technical Sciences","authors":"","doi":"10.17559/tv-20230328000484","DOIUrl":"https://doi.org/10.17559/tv-20230328000484","url":null,"abstract":"","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139774191","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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