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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chunze Yang Xiaodong Zhang Peiyuan Tang Haoran Yuan Haojie Xin Zijiang James Yang School of Computer Science and Technology Xi ’an Jiaotong University Xi’an China School of Computer Science and Technology Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China Technology R&D Department Synkrotron Inc. Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Collecting and annotating data for semantic segmentation can end up costing a lot of time and energy. Unsupervised domain Adaptation (UdA) for semantic segmentation allows models trained on certain source domain data (such as the GTA synthetic dataset) to be applied to certain target data (like the Cityscapes real dataset), significantly decreasing the requirement for target-domain manual pixel-level *** this paper, we suggest a cross-domain unified semantic segmentation network training framework, the Attention distribution Adaptation Network (AdAN). It (1) proposes the Content Consistency Transformation (CCT), which maps source domain data to an intermediate domain that has a data distribution that is comparable to that of the target domain, and (2) introduces the Attention Adaptation Module (AAM), which bolsters the synchronization of attention between the intermediate domain and the target domain. AdAN achieved unprecedented mIoUs of 77.2 and 68.8 on GTA→Cityscapes and Synthia→Cityscapes, respectively, corresponding to improvements of +1.3 and +0.6 over the state of the art.

关键词： Training Adaptation models Semantic segmentation Manuals Signal processing data models Active appearance model Synchronization Speech processing Synthetic data

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research the Traffic Characteristics of MQTT and ddS Protocols in Low-bandwidth radio Networks

Research the Traffic Characteristics of MQTT and DDS Protoco...

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2021 IEEE International Conference on Information and Telecommunication Technologies and radio Electronics, UkrMiCo 2021

作者： Strelkovskaya, Irina Zolotukhin, roman Faculty of Cyber Security and Information Technology National University «Odesa Law Academy» Odesa Ukraine Software Departmen R&D Department LLC «Telecard-Prilad» Odesa Ukraine

ISBN: (纸本)9781665426527

This paper researches the traffic characteristics of Message Queuing Telemetry Transport and data distribution Service protocols from the Object Management Group consortium in low-bandwidth radio networks based on ultra and very high frequency radio stations. The data has been obtained of the number of service packets, the number of lost packets, the maximum size of information packets, the actual amount of information and service data and the time of connection between the nodes of the Message Queuing Telemetry Transport and data distribution Service protocols. © 2021 UkrMiCo 2021 - 2021 IEEE International Conference on Information and Telecommunication Technologies and radio Electronics, Proceedings. All rights reserved.

关键词： Bandwidth

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research on Optimization Algorithm of real Estate Enterprise Value Evaluation Based on BP Neural Network 2

Research on Optimization Algorithm of Real Estate Enterprise...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Niranjanamurthy, M. Saravanan, S. Mahaveerakannan, r. Surendran, S. Nakrani, Tulsidas Arun, M.r. Department of AI and ML BMS Institute of Technology and Management Avalahalli Karnataka Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Tamilnadu Chennai India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Department of Computer Application Sankalchand Patel College of Engineering Sankalchand Patel University Gujarat Visnagar India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India

ISBN: (纸本)9798350305418

Because of its professional skills and expertise, it is able to persuade both the buyer and the selling and make the transaction easier. An optimal design scheme for real estate businesses' worth appraisals is proposed here, and it is based on BPNN (BP neural network). A worth appraisal system is developed by doing an analysis of several worth indicators pertaining to real estate companies, and then the system is optimized with the use of BPNN. In the final step, an analysis of the simulation test is performed. According to the findings of the simulation, the arithmetic has a certain accuracy, which is 8.34% higher than the accuracy of traditional arithmetic. On the premise of ensuring the efficient processing of data in batches and finding the rules therein, reduce the impact of human subjectivity, improve the accuracy and scientificity of real estate appraisal, and provide certain reference for the national and local governments to reasonably regulate house prices, so as to promote the stable operation and healthy development of the real estate market. In doing so, you will be able to promote the growth of the real estate market in a healthy manner. © 2023 IEEE.

关键词： BP neural network Enterprise worth appraisal real estate

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Cancer Genomics: Identifying Biomarkers for Early detection and Treatment

Cancer Genomics: Identifying Biomarkers for Early Detection ...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Husain, Saif O. Kaur, Amandeep Chitra, S. Sharma, Vivek Aminovna, Marupova Manzura rajesh, G. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Department of Information Technology M.P. Bhopal India Fergana Medical Institute of Public Health Fergana Uzbekistan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

Genomic sciences, specifically with regards to cancer, has provided optimism in early diagnosis, treatment options by pointing to the molecular basis of cancer. This work aims at defining biomarkers for cancer diagnosis and treatment - or in other words, discovering novel biomarkers for early detection and treatment of cancer using multi-omics data from TCGA and ICGC. Ing data obtained through genome, transcriptome, and proteome studies , we revealed novel biomarkers corresponding to different types of cancer. The aforementioned datasets were processed with the help of random forests and artificial neural networks because these machine learning techniques delivered high biomarker recognition rates. This will incorporate, biomarkers such as BrCA1 for breast cancer and EGFr for lung cancer, showing high sensitivity and specificity levels. It also tackles issues such as tumor heterogeneity and presents a cardiovascular treatment plan that involves multiple disciplines. Interestingly, the assessment of genomics for cancer diagnosis and treatment has outlined such possible avenues for further investigations and practical use in the future. It is positioned in the rapidly expanding field of personalized oncology;this research seeks to improve patients' lifestyles via the early and accurate identification of cancer. © 2024 IEEE.

关键词： Lung cancer

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distributed Analytics in Smart Grids with Edge Computing and Computational Intelligence

Distributed Analytics in Smart Grids with Edge Computing and...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Apoorva Joshi Preethi d Amandeep Gill Master of Computer Application Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Computer Science Jain (Deemed to be University) Bangalore India Department of R&D Cell Vivekananda Global University Jaipur India

Conventional methods like classification or clustering have limits when it comes to handling the complex task for dispersed data mining across heterogeneous sources and divergent data formats. This is especially true in scenarios where stochastic models or mathematical formulas may not be feasible. data centralization is a prevalent trend that exacerbates problems including communication channel congestion and privacy difficulties related to sensitive data flows. In order to overcome these obstacles, this paper presents the distributed Academic Engine (dAE), an adaptive edge-based scattered analytic engine designed for distributed networks. The dAE routinely breaks down and divides computations that need data from multiple sources among the nodes that are already in place, enabling the sharing of partial results without disclosing the original data. To get the full expression output, the master node combines these partial results. The expression is decomposed and processing jobs are distributed among nodes using three computational intelligence techniques: genetic algorithm, genetic algorithm using evolution control, the particle swarm optimization. This method's effectiveness is illustrated with a case study on smart grid KPI identification, which shows a noteworthy (over 91%) decrease in communication messages over conventional approaches. This demonstrates how well the suggested method works to reduce the difficulties related to distributed data mining.

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Suppression of Crystalline Li15si4 in Silicon-Carbon Composite Anode with a Co-Polymer Binder for Lithium Ion Batteries

SSRN

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SSrN 2024年

作者： Islam, Mohammad A. Han, Sang-don Yang, Junghoon Turallo, Joel Department of Physics State University of New York at Oswego OswegoNY13126 United States Carbon and Light Materials Application R&D Group Korea Institute of Industrial Technology Jeonju54853 Korea Republic of Department of Chemistry Sejong University Seoul05006 Korea Republic of

Suppression of crystalline Li15Si4 in silicon-carbon composite anode in lithium ion battery was achieved with the use of co-polymerized polyimide P84 binder, and a systematic and long-term curing. Electrochemical half-cells with an active material composed of 60% nanosilicon, 20% conductive carbon and 20% P84 binder with a material loading of 1.55 mg/cm2, and lithium metal counter/reference electrode, were cycled between 1.5 V and 50 mV, the latter being the discharge voltage when crystalline Li15Si4 was found to form. Following this, the same cells were cycled between 1.5 V and 10 mV, which is significantly below the threshold of 50 mV. A third set of cycling was carried out on the same cells between 1.5 V and 5 mV. The gravimetric capacity was 1000 mAh/g after ninety cycles with the 50 mV discharge cutoff, it was 450 mAh/g after 200 additional cycles with the 10 mV cutoff, and it was 300 mAh/g after 200 additional cycles with the 5 mV cutoff. Unlike previous reports that showed precipitous drop of capacity accompanied by the formation of crystalline Li15Si4 the capacity drop in these cells was gradual. Cyclic voltammetry measurements carried out on electrochemical half-cells made of the same electrode with various discharge voltage cutoffs demonstrate the absence of sharp delithiation peak at 0.42 V that has been shown to be the hallmark of crystalline Li15Si4. We provide supporting arguments of such absence of crystalline Li15Si4 via X-ray diffraction, raman spectroscopy, field effect scanning electron microscopy/energy dispersive X-ray spectroscopy. © 2024, The Authors. All rights reserved.

关键词： Anodes

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Intelligent Time Management recommendations Using Bayesian Optimization

Intelligent Time Management Recommendations Using Bayesian O...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Chandramowleeswaran, G. Sindhu, M. Alabdeli, Haider Mishra, Anshu Vinod, Priya Kaur, Harmeet Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Administration Chennai600062 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Christ University School Of Business And Management India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India

ISBN: (纸本)9798350390759

This paper focuses on the improvement of the intelligent time management system which employ Bayesian optimization for suggesting time management plans for each particular person. In this sense, through historical data of input-output patterns and users' preferences, the system aims at increasing productivity and user satisfaction. In the study, Gaussian Processes are used as the surrogate model in the Bayesian optimization so that the required evaluations by the algorithm to realize optimal scheduling methodologies are kept to a minimum. Implementation is done as a web application where users submit their tasks and get the recommended schedule instantly. Indicators like, the degree of task accomplishment, time, and scheduling compliance, and probably the users' satisfaction suggest that system helped enhance time management results. Lack of feedback from the users is removed through questionnaire that reveals the simplicity of the system and the quality of its recommended times, thereby supporting the idea of Bayesian optimization as a game changer in the management of time. This research significance points to the need for maintaining efficient and individualized approaches to time management strategies and agrees with others' findings, which suggest that this is an area ample fiction research needs to acknowledge and pursue. © 2024 IEEE.

关键词： Scheduling algorithms

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Effect of Welding Speed on Microstructural Evolution and Strengthening Mechanism of Friction-Stir Welded 7075 Aluminum

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SSrN 2024年

作者： Kim, Jong-Hun Choi, Hyo-Nam Lee, Kwang-Jin Shin, Jae-Hyuck Seo, Nam-Hyuk Jung, Jae-Gil Lee, Seok-Jae Lee, Seung-Joon Gyeonggi-do Siheung-si15073 Korea Republic of Carbon & Light Materials Application R&D Group Korea Institute of Industrial Technology Jeollabuk-do Jeonju-Si54666 Korea Republic of Advanced Materials R&D Department Korea Automotive Technology Institute Chungcheongnam-do Cheonan-Si31214 Korea Republic of Division of Advanced Materials Engineering Jeonbuk National University Jeollabuk-do Jeonju-Si54896 Korea Republic of

In present study, we aim to analyze the microstructural characteristics and strengthening mechanism of AA7075-T6 alloy after the friction-stir welding speeds from 150 to 350 mm min-1. The continuous dynamic recrystallization underwent actively with faster speed, leading to equiaxial and fine grains of 350 specimen, as evidenced by misorientation distribution, grain orientation spread, and aspect ratio. regarding the textural evolution, the 150 specimen retained the deformation texture (e.g., Brass), while the 350 specimen showed the development of recrystallization texture (e.g., Goss). The 250 specimen revealed superior weldability for strength of over ~80% owing to both grain refinement and accumulated dislocations despite the dissolution of precipitates (e.g., GP II zone and ηˊ). © 2024, The Authors. All rights reserved.

关键词： Textures

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Suppression of Crystalline Li15si4 in Silicon-Carbon Composite Anode with a Co-Polymer Binder for Lithium Ion Batteries

SSRN

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SSrN 2024年

关键词： Anodes

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radar target detection method based on improved YOLOv3 5

Radar target detection method based on improved YOLOv3

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IET International radar Conference 2020, IET IrC 2020

作者： Zhongwei, Liu Hongwei, Gao Yingchun, Li rongrong, Xiang Shuyan, Sun dongming, Zhou Jizhou, Yu Radar R&D Center Beijing Institute of Radio Measurement Beijing China Department of Network and Information Security Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781839535635

Aiming at the problems of low detection rate and high false alarm rate in radar target detection under low signal-to-noise ratio environment, a radar target detection method based on improved YOLOv3 is proposed. Considering the characteristics of small scale and few features of radar targets, the original YOLOv3 backbone network is pruned and nonlinear features are added, and the darknet13 network structure is proposed. The multiple outputs of the darknet13 network are fused in two scale, deep fusion is used to detect large-scale targets, and shallow fusion is used to detect small-scale targets. In order to solve the problem of imbalance between positive and negative samples of radar data, the focus loss function is used to strengthen the attention to the hard to distinguish samples. Meanwhile, warm up technology is used to optimize the learning rate adjustment strategy to improve the target detection performance and network training speed. The experimental results show that compared with the original YOLOv3 algorithm, the proposed method can improve the average detection rate by 8.4% and reduce the average false alarm rate by 79.8% when the signal-to-noise ratio is 10 dB. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Feature extraction

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