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software Detection and Warning Method for Pitch Angle Deviation of Road Traffic Radar 9

Software Detection and Warning Method for Pitch Angle Deviat...

引用

9th international conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Sun, Haokai Zhang, Zheng Jari Technology Lianyungang Jari Electronics Co. Ltd Lianyungang China Jiangsu Automation Research Institute Control Science and Engineering Lianyungang China

ISBN: (纸本)9798350376548

Introduced a software detection and warning method for the installation angle of road traffic radar in the pitch direction. Establish a mathematical model M1 based on the directional pattern of the transmitting antenna in the elevation direction. According to the RCS definition, the normalized curve shape of the received target echo signal strength is the same as model M1. the normalization coefficient is determined by on-site calibration methods or empirical value methods, and the model M1 is inversely normalized to obtain the model M2. Substitute the statistical analysis result A of the received signal strength result P ' calculated after real-time and cumulative sampling into model M2 to obtain the current elevation angle information of the radar. If the change in radar pitch angle exceeds the threshold, an alarm message will be output. this method does not require the addition of hardware sensors, and only estimates the current elevation angle of the radar through software calculations based on mathematical models and on-site sampled target echo signal strength, which has good engineering application value. © 2024 IEEE.

关键词： Radar warning systems

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Eclectic Ethical Decision Making for Autonomous Vehicles 9

Eclectic Ethical Decision Making for Autonomous Vehicles

引用

9th international conference on Mobile and Secure Services, MOBISECSERV 2024

作者： Jasti, Pranay Chowdary Griffith, Henry Rathore, Heena Texas State University Department of Computer Science San MarcosTX United States San Antonio College Department of Engineering San AntonioTX United States

ISBN: (纸本)9798350390896

Ensuring the safety of autonomous vehicles (AVs) in morally ambiguous scenarios is critical for widespread trust and adoption of them. the existing solutions for moral decision-making in AVs are constrained by their methods of handling ethical theories. Specifically, reinforcement learning (RL) approaches generate rewards based on random weights (credence) assigned to different ethical theories, which does not adequately manage uncertainty. Additionally, other approaches like predictive controllers and ethical valence theory rely on preset priorities, which also fail to effectively address the inherent uncertainty in morally ambiguous scenarios. this work proposes a novel approach using RL to managing uncertainty and integrating evidence from multiple ethical theories, aiming to improve the ethical decision-making capabilities of AVs. By combining the simplex algorithm with the Dempster-Shafer (DS) technique, we integrate the theories into our framework to represent and reason with uncertain information. the simplex algorithm optimizes the reward structure in RL, and its results inform the DS technique to generate credence. Our approach outperforms existing models, addressing ethical challenges in AVs effectively. © 2024 IEEE.

关键词： Adversarial machine learning

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YET ANOthER LEAST SQUARES ESTIMATION IN NHPP-BASED software RELIABILITY models WIth GROUPED DATA 14

YET ANOTHER LEAST SQUARES ESTIMATION IN NHPP-BASED SOFTWARE ...

引用

14th international conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Wu, Jingchi Daido, Koutaro Dohi, Tadashi Okamura, Hiroyuki Hiroshima University Higashi-Hiroshima Japan

ISBN: (纸本)9781837242672

the present paper concerns yet another least squares estimation in nonhomogeneous Poisson process (NHPP)-based software reliability models. As Ishii et al. (2012) pointed out in the analysis of software fault-count time-domain data, since the ordinary least squares (OLS) estimation is irrelevant to the probability law of the underlying NHPP, its applicability to estimate the model parameters as well as the quantitative software reliability is rather questionable. We consider similar but somewhat different least squares estimation methods for the software fault-count time-interval data, called the group data, and compare their predictive performances with the OLS estimation and the common maximum likelihood (ML) estimation. In numerical illustrations with eleven parametric NHPP-based software reliability models, it is shown that both the OLS estimation and the ML estimation could not always provide the best predictors. © the Institution of engineering & Technology 2024.

关键词： Digital elevation model

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Adaptive Retraining of AI/ML Model for Beyond 5G Networks: A predictive Approach 9

Adaptive Retraining of AI/ML Model for Beyond 5G Networks: A...

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9th IEEE international conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Gudepu, Venkateswarlu Chintapalli, Venkatarami Reddy Castoldi, Piero Valcarenghi, Luca Tamma, Bheemarjuna Reddy Kondepu, Koteswararao Indian Inst Technol Dharwad Dharwad India Natl Inst Technol Calicut Calicut India Scuola Super Sant Anna Pisa Italy Indian Inst Technol Hyderabad Hyderabad India

ISBN: (纸本)9798350399806

Beyond fifth-generation (B5G) networks (namely 6G) aim to support high data rates, low-latency applications, and massive machine communications. Integrating Artificial Intelligence (AI) and Machine Learning (ML) models are essential for addressing the network's increasing complexity and dynamic nature. However, dynamic service demands of B5G cause the AI/ML models performance degradation, resulting in violations of Service Level Agreements (SLA), over- or under-provisioning of resources, etc. To address the performance degradation of the AI/ML models, retraining is essential. Existing threshold and periodic retraining approaches have potential disadvantages such as SLA violations and inefficient resource utilization for setting a threshold parameter in a dynamic environment. this paper presents a novel algorithm that predicts when to retrain AI/ML models using an unsupervised classifier. the proposed predictive approach is evaluated for a Quality of Service (QoS) prediction use case on the Open RAN software Community (OSC) platform and compared to the threshold approach. the results show that the proposed predictive approach outperforms the threshold approach.

关键词： Prediction AI/ML Retraining 5G and Beyond

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Multi-Objective Optimization of Small Centrifugal Pumps for Liquid Hydrogen Transport Applications Using CFD Tools 9

Multi-Objective Optimization of Small Centrifugal Pumps for ...

引用

9th international engineering, Sciences and Technology conference, IESTEC 2024

作者： De La Hoz, Jose M. Sanchez Marquez, Juan S. Mosquera Toscano, Andres D. Rodriguez Mendoza, Wilson A. Beleno Jimenez, Jorge L. Miranda De Luque Rojas, Osvaldo E. Universidad de la Costa Department of Energy Barranquilla Colombia Universidad Del Norte Department of Engineering Barranquilla Colombia

ISBN: (纸本)9798350377156

Due to the climate change effects and high fossil fuel consumption to supply the global energy demand, scientific studies have been conducted to reduce the pollutant emissions and improve the energy generation processes. In this sense, cryogenic flow transport processes for hydrogen and electrical energy generation have promoted the application of alternative fuels under complex working conditions to reduce greenhouse gas emissions. With the aim to analyze and optimize the application of mechanical devices for liquid hydrogen transport processes, this research proposes a predictive analysis of a cryogenic pumps based on numerical simulations with CFD tools in a virtual environment. To achieve accurate results, turbulence models are applied to study and predict the liquid hydrogen behavior and its interaction with impeller blades of centrifugal pumps. In consideration with the complex analysis of turbulent and cryogenic flows, a CFD tool is computed to solve and describe the workflow behavior around the blades under different working conditions. Finally, a multi-objective optimization method based on CFD simulations is proposed to improve the impeller design of the small centrifugal pump and reduce costs with computational tools. © 2024 IEEE.

关键词： Impellers

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Research on software Fault Feature Data Extraction Method for software Fault Prediction Technology 9

Research on Software Fault Feature Data Extraction Method fo...

引用

9th international conference on Advances in Machinery, Materials Science and engineering Applications, MMSE 2023

作者： Yan, Ran Wang, Meichen Xu, Zhaowei Zhang, Kai China Shipbuilding Software Quality & Reliability Testing Center Beijing China

ISBN: (纸本)9781643684383

the correlation between software failure characteristics and software failure directly determines the predictive performance of the failure prediction model. the extraction of software fault features is crucial for building equipment software fault prediction models, and is an important process to ensure accurate prediction. However, the software fault feature data extraction method is often complicated to use and has no pertinence to the selected software fault feature data, and it takes a lot of time to complete the extraction steps. this paper summarizes software metrics and software defect types based on research at home and abroad, and selects software metrics and software defect types that are suitable for equipment software. Using regular expression technology and CSV technology research the automatic extraction way of software fault features, and finally constructs a fault data set that can be used for software fault prediction models. © 2023 the Authors.

关键词： Extraction

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Utilizing Machine Learning predictive Analytics to Enhance Early Sepsis Diagnosis in Critical Care Setting 9

Utilizing Machine Learning Predictive Analytics to Enhance E...

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9th IEEE/ACIS international conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Daothong, Paron Jampa-Ngern, Sira Senavongse, Wongwit The Graduate School Srinakharinwirot University Department of Biomedical Engineering Nakhon Nayok Thailand Srinakharinwirot University Faculty of Engineeringl Department of Biomedical Engineering Nakhon Nayok Thailand

ISBN: (纸本)9798350394191

Sepsis remains a critical medical challenge worldwide, with millions of cases and fatalities annually, particularly affecting vulnerable populations like children. the evolving understanding of sepsis has led to refinements in diagnostic criteria, including the introduction of novel metrics like qSOFA. Despite advancements, early diagnosis remains pivotal for improved outcomes. this study leverages electronic medical records (EMR) and machine learning to propose a predictive model for early sepsis diagnosis in critical care patients. Using the eICU Collaborative Research Database, the dataset comprising vital signs was preprocessed and balanced. Various machine learning algorithms, including deep learning models, were evaluated using 10-fold cross-validation. Performance metrics are AUROC, accuracy, F1 score, and Brier score were computed to assess model effectiveness. XGBoost emerged as the top-performing algorithm, exhibiting superior performance across metrics compared to other models with AUROC = 0.78, Accuracy = 80, F1-Score = 72, Brier-Score = 0.20. While demonstrating promising results, XGBoost exhibited higher false-positive rates, indicating the need for further refinement. Overall, the study underscores the potential of machine learning in early sepsis diagnosis, providing insights for future optimization and clinical application. © 2024 IEEE.

关键词： Electronic health record

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Impact of Excitation Signal on a predictive Model Used to Harness Energy Flexibility of a Dwelling 9th

Impact of Excitation Signal on a Predictive Model Used to Ha...

引用

9th international Building Physics conference

作者： Erfani, Arash Jafarinejad, Tohid Roels, Staf Saelens, Dirk Dept Civil Engn Bldg Phys & Sustainable Design Kasteelpk Arenberg 40 B-3001 Leuven Belgium EnergyVille Thor Pk 8310 B-3600 Genk Belgium

ISBN: (纸本)9789819783113;9789819783090;9789819783083

To increase the share of renewable energy sources in the energy mix, buildings must provide energy flexibility to the grid. Model predictive Control (MPC) has proven to be instrumental in activating the energy flexibility of buildings. MPC requires a predictive model to capture the thermal behavior of a building. these models require operational data from the building. the quality of these datasets affects the predictive model's performance and, in turn, the energy flexibility. A dataset could be obtained by exciting the building using conventional controllers or randomly generated signals. Using a random excitation causes thermal discomfort for the occupants while using data from a traditional controller might not generate a dataset suitable to train predictive models. this study aims to assess the effect of the excitation signal of a dataset used for training an MPC's predictive model to harness a dwelling's energy flexibility. the under-study dwelling is equipped with underfloor heating coupled with an air-to-water heat pump. First, building is excited with three excitation signals: a Pseudo-Random Binary Sequence (PRBS), Rule-Based Controller (RBC) and Random Set-point Tracking (RST). Second, predictive models trained by each excitation signal are employed in an MPC framework to maximize the energy flexibility of a dwelling. the findings reveal that it is feasible to excite a building in a way that the thermal comfort of the occupants is guaranteed while the identification dataset is rich enough to generate a robust predictive model. Such predictive model would lead to high energy flexibility activation of the dwelling.

关键词： Model predictive control Energy flexibility excitation signal

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software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT 24

Software Vulnerability Prediction in Low-Resource Languages:...

引用

28th international conference on Evaluation and Assessment in software engineering (EASE)

作者： Triet Huynh Minh Le Babar, M. Ali Tung Hoang thai Univ Adelaide CREST Ctr Res Engn Software Technol Adelaide SA Australia Cyber Secur Cooperat Res Ctr Joondalup Australia

ISBN: (纸本)9798400717017

Background: software Vulnerability (SV) prediction in emerging languages is increasingly important to ensure software security in modern systems. However, these languages usually have limited SV data for developing high-performing prediction models. Aims: We conduct an empirical study to evaluate the impact of SV data scarcity in emerging languages on the state-of-the-art SV prediction model and investigate potential solutions to enhance the performance. Method: We train and test the state-of-the-art model based on CodeBERT with and without data sampling techniques for function-level and line-level SV prediction in three low-resource languages - Kotlin, Swift, and Rust. We also assess the effectiveness of ChatGPT for low-resource SV prediction given its recent success in other domains. Results: Compared to the original work in C/C++ with large data, CodeBERT's performance of function-level and line-level SV prediction significantly declines in low-resource languages, signifying the negative impact of data scarcity. Regarding remediation, data sampling techniques fail to improve CodeBERT;whereas, ChatGPT showcases promising results, substantially enhancing predictive performance by up to 34.4% for the function level and up to 53.5% for the line level. Conclusion: We have highlighted the challenge and made the first promising step for low-resource SV prediction, paving the way for future research in this direction.

关键词： software vulnerability software security Large language models ChatGPT Empirical study

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software engineering Comments Sentiment Analysis Using LSTM with Various Padding Sizes 18

Software Engineering Comments Sentiment Analysis Using LSTM ...

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Vijayvargiya, Sanidhya Kumar, Lov Murthy, Lalita Bhanu Misra, Sanjay Krishna, Aneesh Padmanabhuni, Srinivas BITS Pilani Hyderabad Telangana India NIT kurukshetra Thanesar India Ostfold Univ Coll Halden Norway Curtin Univ Bentley WA Australia Testaing Com Hyderabad Telangana India

ISBN: (纸本)9789897586477

Sentiment analysis for software engineering(SA4SE) is a research domain with huge potential, with applications ranging from monitoring the emotional state of developers throughout a project to deciphering user feedback. there exist two main approaches to sentiment analysis for this purpose: a lexicon-based approach and a machine learning-based approach. Extensive research has been conducted on the former;hence this work explores the efficacy of the ML-based approach through an LSTM model for classifying the sentiment of the text. three different data sets, StackOverflow, JIRA, and AppReviews, have been used to ensure consistent performance across multiple applications of sentiment analysis. this work aims to analyze how LSTM models perform sentiment prediction across various kinds of textual content produced in the software engineering industry to improve the predictive ability of the existing state-of-the-art models.

关键词： Sentiment Analysis Deep Learning Data Imbalance Methods Feature Selection Classification Techniques Word Embedding

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