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Research on compiler version recognition based on random forest algorithm

Research on compiler version recognition based on random for...

引用

IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Xinyun Hu Yuxian Tang School of Mathematics & Statistics Anhui Normal University Wuhu China School of Physics and Electronic Information Anhui Normal University Wuhu China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

Compiler version recognition is essential in the realm of computer tools, not only facilitating comprehension ofthe compilation process but also offering crucial guidance for optimization endeavors. this study delves into an exhaustive exploration of the distinctions among various versions of $\mathbf{C}++$ compilers in GCC when compiling specific program source code. the analysis aims to precisely pinpoint the compiler version being used. To achieve this, we compile the source code in Annex 1 using different versions of C++ compilers in GCC and meticulously scrutinize the differences in compilation outcomes, encompassing aspects such as instruction sequence, optimization strategy, and code structure. Leveraging techniques from software engineering like judgment analysis, disassembly, and static analysis, we identify the distinctive characteristics of the code generated by diverse compilers and highlight the key features that serve as significant discriminators between varying compiler versions. Taking a step further, we introduce a novel model for compiler version recognition that leverages deep learning methodologies. through the collection of a substantial volume of code samples produced by different compiler versions and employing the decision tree model for training, our model demonstrates exceptional performance on both the training set and the test set. It excels in accurately discerning the code generated by distinct compiler versions.

关键词： Training Analytical models Codes Accuracy Source coding predictive models Feature extraction Decision trees Random forests Optimization

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Assessment of Demand Response Potential for Commercial Air Conditioning Based on Battery Energy Storage Strategy

Assessment of Demand Response Potential for Commercial Air C...

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Electrical engineering and Control Technologies (CEECT), conference on

作者： Li Zhang Shaodi Zhang Chenggang Cui Hui Chen Pinpin Zhou Shanghai University of Electric Power Shanghai China Shanghai Electrical Apparatus Research Institute (Group) Co. Shanghai China

ISBN: (数字)9798331528096

ISBN: (纸本)9798331528102

With rising global energy demand, air conditioning systems in commercial buildings are crucial for managing peak loads on the power grid. this paper evaluates the potential of these systems in demand response under time-of-use pricing, focusing on a battery energy storage strategy. By developing thermal models for buildings, battery storage, and human comfort, and using model predictive control, the study assesses the performance of battery energy storage systems in reducing electricity costs and enhancing demand response capabilities. Simulation results show that the battery energy storage strategy (BESS) achieved a 220 kW load reduction during demand response periods, with a 100% reduction rate, significantly surpassing the $\mathbf{1 7. 7 \%}$ reduction of the pre-cooling combined with global temperature adjustment (PCGAT) strategy. Additionally, the BESS strategy reduced daily electricity costs by approximately 33.88% and generated an extra 1320 yuan in demand response incentives, all while keeping user dissatisfaction below 5.74%.

关键词： Air conditioning Electric potential Costs Atmospheric modeling Electricity Buildings predictive models Demand response Batteries Load modeling

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Optimization of Public Electricity Supply Station Based on Electrifying Lifestyle Area

Optimization of Public Electricity Supply Station Based on E...

引用

international conference on Power engineering and Renewable Energy (ICPERE)

作者： Ariandu Pradita Octoviandiningrum Hadi Anggi Adilta Wijaya Adhi Herlambang Nanang Hariyanto School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia PT. PLN (Persero) Bandung Indonesia Distribution Main Unit PT. PLN (Persero) Jakarta Indonesia

ISBN: (数字)9798331504694

ISBN: (纸本)9798331504700

this research aims to optimize the Public Electricity Supply Station (PESS) based on the Electrifying Lifestyle Area concept to address the issue of electricity oversupply in Indonesia and increase energy sales by the State-owned Electricity Company (SEC) in Indonesia, while reducing the use of conventional diesel generators by street vendors. Using both qualitative and quantitative methods, this study analyzes the strategic placement of PESS, power flow simulations using ETAP software, and the reliability of the electrical system in the Tugu Keris Siginjai area, Jambi. the results show that between January and December 2023, energy sales from PESS reached 20,098 kWh with a revenue of IDR 33,051,563. this research emphasizes the importance of educating the street vendors and public on environmentally friendly electricity usage as an effort to change people's lifestyles, highlighting a novelty at the implementation of the Electrifying Lifestyle concept in raising awareness of clean energy in commercial areas.

关键词： Green energy Power engineering Analytical models Electricity Companies software Generators software reliability Optimization Load flow

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Research On Structural Safety Analysis Method Of Civil Aircraft

Research On Structural Safety Analysis Method Of Civil Aircr...

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System Reliability and Safety engineering (SRSE), international conference on

作者： HaiJian Xiao TianQin Song MingHui Gu BaoHui Jia College of Safety Science and Engineering Civil Aviation University of China Tianjin China College of Transportation Science and Engineering Civil Aviation University of China Tianjin China

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

the safety analysis and evaluation of civil aircraft run through the entire life cycle of the aircraft. Based on the characteristics of the operational environment and damage to civil aircraft structures, a continuous safety risk assessment process for civil aircraft structures was established based on SAE 5150. From the perspective of Probability Damage Tolerance Analysis (PDTA), the data characteristics of civil aircraft structures in the design, manufacturing, and operation stages were considered, and a probability prediction model for the structure was established using the MC method. Taking the civil aircraft keel beam crack event as an example, the safety of structural crack events was systematically evaluated, and the risk level charts were proposed that can simultaneously achieve risk level determination and determine the implementation time limit of corrective measures. Compared with the traditional risk assessment methods, the results of the determination area map based on the “Gunston” method are more accurate and intuitive.

关键词： Atmospheric modeling Tolerance analysis predictive models Time measurement Data models Safety Aircraft manufacture Risk management Reliability Aircraft

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Reliability and Timeliness of Servicing Requests in Infocommunication Systems, Taking into Account the Physical and Information Recovery of Redundant Storage Devices 6

Reliability and Timeliness of Servicing Requests in Infocomm...

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6th international Scientific conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Bogatyrev, Vladimir A. Bogatyrev, Stanislav V. Bogatyrev, Anatoly V. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia Yadro Cloud Storage Development Center Saint Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

Markov models of reliability of fault-tolerant computer systems are proposed, taking into account two stages of recovery of redundant memory devices. At the first stage, the physical recovery of memory devices is implemented, and at the second, the informational one consists in entering the data necessary to perform the required functions. Memory redundancy is carried out to increase the stability of the system to the loss of unique data generated during the operation of the system. Data replication is implemented in all functional memory devices. Information recovery is carried out using replicas of data stored in working memory devices. the model takes into account the criticality of the system to the timeliness of calculations in real time and to the impossibility of restoring information after multiple memory failures, leading to the loss of all stored replicas of unique data. the system readiness coefficient and the probability of its transition to a non-recoverable state are determined. the readiness of the system for the timely execution of requests is evaluated, taking into account the influence of the shares of the distribution of the performance of the computer allocated for the maintenance of requests and for the entry of information into memory after its physical recovery. © 2022 IEEE.

关键词： Recovery

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Road Distress Identification Using GPR Signals and U-Net Model

Road Distress Identification Using GPR Signals and U-Net Mod...

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Qian Liu Zhen Liu The college of electrical engineering Zhejiang University of Water Resources and Electric Power Hangzhou China College of Engineering The Pennsylvania State University University Park PA USA

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Ground Penetrating Radar (GPR) stands out as an efficacious modality for identifying concealed structural impairments within highway subgrade. Its merits include expeditious data acquisition, superior resolution, and robust resistance to interference. Nonetheless, the traditionally slow pace and inconsistent proficiency associated with manual interpretation prompted the development of an alternative approach expounded in this study. Leveraging the U-net network, this model in question is designed to identify GPR images, with an emphasis on detecting typical defects in road subgrades as a representative example. Empirical findings showcase a commendable Mean Intersection over Union (MIoU) rate of 85%, attesting to the adept training of the model. this methodology demonstrably enhances the speed and precision of GPR data interpretation, exhibiting notable predictive efficacy when applied to both simulated datasets and authentic subterranean cavity measurements.

关键词： Training Resistance Image resolution Roads Manuals Interference Signal processing Data models Robustness Maintenance

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Uncertainty-based Prediction of Wear Resistance for Brake Pad under Braking Conditions

Uncertainty-based Prediction of Wear Resistance for Brake Pa...

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System Reliability and Safety engineering (SRSE), international conference on

作者： Lu Yao Xintian Liu Jiao Luo Taotao Cheng School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

Brake pads are an important part of the brake pair, excessive wear will affect the braking performance of the car, which is very unfavorable to driving safety. the wear of brake pads is complex, non-linear and multi-dimensional. therefore, this paper uses genetic algorithm to improve the initial weight and threshold of BP neural network, and builds a brake pad wear prediction model based on braking conditions with test data as samples. Moreover, considering the uncertainty and inaccuracy of data collection, the wear process is predicted with uncertainty. the simulation results show that the GA-BP neural network optimized by genetic algorithm has faster convergence speed and higher prediction accuracy than BP neural network, and the probability that the interval prediction value obtained by uncertain prediction contains the predicted value and the actual value is 100%, which improves the reliability of prediction results.

关键词： Resistance Uncertainty Simulation Neural networks predictive models Reliability engineering Safety Brakes Genetic algorithms Convergence

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A Comprehensive Study Of the Machine Learning With Federated Learning Approach For Predicting Heart Disease

A Comprehensive Study Of The Machine Learning With Federated...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Pakhi Sharma Sanjiv Sharma Computer Science and Engineering KIET Group of Institutions Ghaziabad India

Heart disease is a leading cause of mortality worldwide, resulting in millions of deaths annually. As individuals age and their physical condition deteriorates, the risk of developing heart disease increases. To mitigate this risk, predictive models leveraging machine learning and artificial intelligence have emerged as valuable tools for early diagnosis and treatment. In this review paper, we introduce the Google-pioneered concept of federated learning as a means to address concerns about data safety in the context of heart disease prediction. Federated learning, also known as collaborative learning, employs a technique wherein an algorithm is trained through multiple independent sessions, each utilizing its own dataset. this paper aims to provide a comprehensive investigation of recent machine learning approaches and databases employed in predicting the occurrence of cardiovascular disease.

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Wind Power Forecasting Based on CNN-LSTM-Attention

Wind Power Forecasting Based on CNN-LSTM-Attention

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Electrical, Electronic Information and Communication engineering (EEICE), international conference on

作者： Wencan Zhang Jianwei Huang Yanyu Huang Tongqiang Chen Maritime College Fujian Chuanzheng Communications College Fuzhou China College of Transportation and Navigation Quanzhou Normal University Quanzhou School of Mechanical and Electrical Engineering and Automation Foshan University Foshan China

ISBN: (数字)9798331532598

ISBN: (纸本)9798331532604

To improve the accuracy of wind power forecasting, this paper proposes an integrated model combining a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and an Attention mechanism. First, using a wind power dataset provided by the Guohua Xinjiang Wind Farm, data preprocessing was performed, including generating samples with a sliding window approach and applying normalization. then, CNN was used to extract local features, LSTM was used to capture temporal dependencies, and the Attention mechanism was employed to dynamically assign feature weights. Finally, predictions were made on the test set, and the model’s performance was evaluated. Experimental results show that the CNN-LSTM-Attention model outperforms traditional CNN, LSTM, and CNN-LSTM models in terms of RMSE, MAE, MAPE, and R 2 indicators. this demonstrates the effectiveness of CNN-LSTM-Attention in complex time-series forecasting and provides an efficient and reliable solution for wind power forecasting, offering theoretical and technical support for wind farm operations optimization.

关键词： Attention mechanisms Accuracy Wind power generation predictive models Wind farms Feature extraction Stability analysis Convolutional neural networks Forecasting Long short term memory

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Interrupted Track Association Based on CNN-BiLSTM Bidirectional Prediction and Fuzzy Analysis

Interrupted Track Association Based on CNN-BiLSTM Bidirectio...

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Electrical, Electronic Information and Communication engineering (EEICE), international conference on

作者： Jixian He Ruihui Peng Dianxing Sun Xueting Yang College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331532598

ISBN: (纸本)9798331532604

With the advancement of modern multi-sensor tracking technology, track association plays a critical role in applications such as target tracking and air traffic control. To address the issue of cross-regional track interruptions in multi-radar cooperative systems, this paper proposes an interrupted track association method based on CNN-BiLSTM bidirectional prediction and fuzzy analysis. the method utilizes Convolutional Neural Networks (CNNs) to extract spatial features of track data, and employs Bidirectional Long Short-Term Memory (BiLSTM) networks to perform temporal modeling by predicting the future trajectory of the target forward in time and inferring its historical trajectory backward in time. this approach enables more accurate prediction and recovery of the target's trajectory during interruption periods. In addition, a fuzzy logic analysis mechanism is introduced to enhance the robustness of the algorithm in handling uncertainty by establishing fuzzy logic rules to quantify ambiguity in data association. Experimental results demonstrate that the proposed method effectively reduces the probabilities of missed associations and false associations.

关键词： Fuzzy logic Target tracking Accuracy Uncertainty Bidirectional long short term memory predictive models Prediction algorithms Feature extraction Robustness Trajectory

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