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第七届工程管理国际学术研讨会

作者： Haizhu LI Wenze GAO Xuancang WANG School of Highway Chang'an University

The detection of abnormal low-price bidding behavior is one of the widely concerned problems in the field of construction project bidding. In practice, the method of setting abnormal quotation threshold based on experience is often unconvincing due to the lack of strong theoretical support. For this reason, from the point of view of mathematical statistics theory, this paper proposes a rapid detection model of abnormal low-price bidding based on the sample size of different bidders' quotations by using the method of hypothesis test. According to the number of bidders, the bidding quotation set is divided into large sample data and small sample data. For large sample data, it is assumed that bidding quotation obeys normal distribution, and the abnormal quotation interval is obtained by t-test of samples;for small sample data, Shapiro-Wilk method is used to test whether bidding quotation obeys normal distribution. If the quotation data obeys normal distribution at the lower 0.05 significant level, the sample data is treated as positive-too distribution, otherwise, Chebyshev theorem is used to discriminate. The model can quickly and accurately determine the objective abnormal quotation threshold, and can solve the problem of distinguishing abnormal low-price bidding behavior in the field of construction project bidding.

关键词： Bidding for Construction Projects Abnormally low price bidding detection algorithm contracting authority

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Android malicious code detection and recognition based on depth learning

Android malicious code detection and recognition based on de...

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2017 4th International Conference on Machinery,Materials and Computer(MACMC 2017)

作者： YangJing Hubei University of Police

ISBN: (纸本)9781510871038

At present, most malicious code detection methods are based on the shallow machine learning model. These shallow machine learning methods are simple in the modeling process, and restrict the complex functions and classification problems. In order to improve the accuracy of Andriod malicious code detection and recognition, an algorithm of malicious code detection and recognition of the deep learning has been put forward in this paper, this algorithm based on neural network training and learning model. Through the learning and training of malicious code sample data, the static, dynamic characteristics and malicious application characteristics of malicious code data are analyzed, including privilege feature, API feature and Op Codes characteristic data. The comprehensive performance of the algorithm was tested, the test results indicate that using depth learning detection algorithm, Andriod malicious code identification accuracy is higher, and false detection rate and undetected rate are low, which is a highly efficient and reliable malicious code detection and recognition algorithm.

关键词： malicious code detection algorithm depth learning neural network Andriod terminal

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Modeling of Wood Macrostructure in the Technology of Its Cutting 7th

Modeling of Wood Macrostructure in the Technology of Its Cut...

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7th International Conference on Industrial Engineering, ICIE 2021

作者： Isaev, S. Erenkov, O. Pacific National University 136 Tikhookeanskaya Street Khabarovsk680035 Russia

ISBN: (纸本)9783030852290

The work presents general approaches to the construction of the software and hardware stable complex. The complex is capable to detect textural annual rings of round timber with subsequent the object internal macrostructure reconstruction based on the data obtained. The complex constructions are based on algorithms for processing binary and grayscale images using algorithms for identifying informative features and applying the Hough transform. The complex using is aimed to provide tools for simplifying the round timber cutting process and visualizing of its cutting results in the required form. The work reflects the results of creating the round timber macrostructure reconstruction and visualization model with the functions variety for changing the shape and size of the object. The possibility of constructing sections both in 2D and in 3D is achieved also it is possible to obtain the surfaces projections in the plane of sections. Such results allow visually evaluate the round timber texture pattern in a specific coordinate plane in the of round timber volume. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cutting Technology detection algorithm Imitation Macrostructure Modeling Software Module Wood

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Overdose detection Among High-Risk Opioid Users Via a Wearable Chest Sensor in a Supervised Injecting Facility: Protocolfor an Observational Study

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JMIR RESEARCH PROTOCOLS 2024年 13卷 e57367页

作者： Tas, Basak Lawn, Will Jauncey, Marianne Bartlett, Mark Dietze, Paul O'Keefe, Daniel Clark, Nico Henderson, Bruce Cowan, Catriona Meredith, Osian Strang, John Kings Coll London Inst Psychiat Psychol & Neurosci Natl Addict Ctr Addiction Sci Bldg London SE5 8BB England Kings Coll London Inst Psychiat Psychol & Neurosci Dept Psychol London England Medically Supervised Injecting Ctr Sydney Australia Burnet Inst Dis Eliminat Melbourne Australia Royal Melbourne Hosp Melbourne Australia PneumoWave LTD Motherwell Scotland South London & Maudsley NHS Fdn Trust London England

Background: Opioid overdose is a global health crisis, affecting over 27 million individuals worldwide, with more than 100,000 drug overdose deaths in the United States in 2022-2023. This protocol outlines the development of the PneumoWave chest biosensor, a wearable device being designed to detect respiratory depression in real time through chest motion measurement, intending to enhance early intervention and thereby reduce fatalities. Objective: The study aims to (1) differentiate opioid-induced respiratory depression (OIRD) from nonfatal opioid use patterns to develop and refine an overdose detection algorithm and (2) examine participants' acceptability of the chest biosensor. Methods: The study adopts an observational design over a 6-month period. The biosensor, a small device, will be worn by consenting participants during injecting events to capture chest motion data. Safe injecting facilities (SIF) in Melbourne, Victoria (site 1), and Sydney, New South Wales (site 2), which are legally sanctioned spaces where individuals can use preobtained illicit drugs under medical supervision. Each site is anticipated to recruit up to 100 participants who inject opioids and attend the SIF. Participants will wear the biosensor during supervised injecting events at both sites. The biosensor will attempt to capture data on an anticipated 40 adverse drug events. The biosensor's ability to detect OIRD will be compared to the staff-identified events that use standard protocols for managing overdoses. Measurements will include (1) chest wall movement measured by the biosensor, securely streamed to a cloud, and analyzed to refine an overdose detection algorithm and (2) acute events or potential overdose identified by site staff. Acceptability will be measured by a feedback questionnaire as many times as the participant is willing to throughout the study. Results: As of April 2024, a total of 47 participants have been enrolled and data from 1145 injecting events have already been

关键词： wearable sensor overdose opioid-related deaths injecting opioid use medically supervised injection center opioid inducedrespiratory depression mobile phone opioid overdose drug overdose Australia United States chest biosensor biosensor wearable device respiratory depression algorithm detection algorithm observational design illicit drugs safe injecting facilities naloxone wearable

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Research on Safety Quality Monitoring algorithm of Assembled Building Construction Based on Neural Network Technology

Research on Safety Quality Monitoring Algorithm of Assembled...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Zhang, Shuaishuai Liu, Huan Wang, Huaizhu Shandong Polytechnic College Shandong China

ISBN: (纸本)9798350315455

With the continuous development of the city, prefabricated building has gradually become an important form of modern architecture, but at the same time, the construction safety of prefabricated building has increasingly attracted people's attention. Therefore, this paper aims to solve the problem of safety and quality monitoring during the construction of prefabricated building, and proposes a monitoring algorithm based on neural network technology. Through the collection and analysis of multiple safety and quality data in the construction process, a neural network model was established, and the superiority of the model in accuracy, recall, F1 value, AUC value and other indicators was verified. In terms of accuracy, the neural network technology was 0.82, which was significantly improved compared with 0.74 of the benchmark models. The research results provide a new safety and quality monitoring solution for the construction of prefabricated building, and have important practical significance. © 2023 IEEE.

关键词： assembled building construction detection algorithm neural network safety and quality

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Study of Low-altitude Slow and Small Target detection on Radar

Study of Low-altitude Slow and Small Target Detection on Rad...

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2017 5th International Conference on Machinery,Materials and Computing Technology(ICMMCT 2017)

作者： Daoming Xu Hongwei Zhang Department of Electronic and Optics Engineering Ordnance Engineering College

Holding low-altitude, slow and small(LSS) target has become a difficulty and study hotspot in air defense warning. This paper firstly introduces the characteristics of the LSS-target and the practical significance and military requirements for detecting such target, and briefly describes the difficulty of detecting radar targets. Then, finally, the prospects for the study of the LSS-target detection are presented.

关键词： LSS-target detection technology Clutter suppression detection algorithm

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Mit :: Lcs :: Tr :: Mit-Lcs-Tr-326

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2016年

Mit :: Lcs :: Tr :: Mit-Lcs-Tr-326 by published by

关键词： action algorithm call chain call message deadline deadline extension detection detection algorithm guardian handler handler action handler call identifier local root message mit-lcs-tr-326 orphan orphan detection purely local root action topaction

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Mit :: Lcs :: Tr :: Mit-Lcs-Tr-736

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2016年

[Auto Generated] 1 Introduction 1 1.1 The BeBe System 1 2 Background 2 2.1 Computational architectures 2 2.2 Continuous speech 4 2.3 Phoneme models 6 2.4 Human speech recognition 6 2.5 Students learn phonemes 7 3 Design 8 4 Implementation 9 5 Results 12 5.1 Comparison of others 14 6 Discussion 15 6.1 BeBe as the first stage 16 6.2 Recognizing vowels 17 6.3 Assessment and future work 18 References 19 The task of speech recognition is to map a digitally encoded signal to a string of words. Over th

关键词： algorithm bebe bebe architecture bebe system detection detection algorithm detection algorithms detector detectors human speech mit-lcs-tr-736 phoneme phoneme detectors phonemes phonetic phonetic transcript recognition spectral density speech speech recognition speech sounds

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The notion of "best predictors": An application to tornado prediction

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WEATHER AND FORECASTING 1999年第6期14卷 1007-1016页

作者： Marzban, C Mitchell, ED Stumpf, GJ Univ Oklahoma Natl Severe Storms Lab Norman OK 73069 USA Univ Oklahoma Cooperat Inst Mesoscale & Meteorol Studies Norman OK 73069 USA Univ Oklahoma Dept Phys Norman OK 73069 USA

It is argued that the strength of a predictor is an ill-defined concept. At best, it is contingent on many assumptions, and, at worst, it is an ambiguous quantity. It is shown that many of the contingencies are met (or avoided) only in a bivariate sense, that is, one independent variable (and one dependent variable) at a time. Several such methods are offered after which data produced by the National Severe Storms Laboratory's Tornado detection algorithm are analyzed for the purpose of addressing the question of which storm-scale vortex attributes based on Doppler radar constitute the "best predictors" of tornadoes.

关键词： .Bivariate tornadoes ATTRIBUTES National Severe Storms Mesoscale and Meteorological detection algorithm Application to Tornado Best Predictors contingencies Storms Laboratory Tornado statistical model

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A Review of Cybersecurity Challenges in Smart Power Transformers

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IEEE ACCESS 2024年 12卷 193972-193996页

作者： Rahimpour, Hossein Tusek, Joe Musleh, Ahmed S. Liu, Boyu Abuadbba, Alsharif Phung, Toan Seneviratne, Aruna Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Ampcontrol Warabrook NSW 2304 Australia CSIROs Data61 Marsfield NSW 2122 Australia Cyber Secur Cooperat Res Ctr Joondalup WA 6027 Australia

In the evolving landscape of critical infrastructure security, the increasing sophistication of cyber threats poses challenges, particularly to the power grid's new technology infrastructure. Despite traditionally being considered passive elements, power transformers, as critical components of the grid, are now equipped with smart technologies and intelligent monitoring systems. These enhancements, while beneficial for asset management, inadvertently increase their vulnerability to cyber threats. Existing literature predominantly focuses on the cybersecurity of smart grids, mainly utilising model-based detection methods. Yet, there's a noticeable gap in research on the cybersecurity of critical power components, notably power transformers, within the network. Our paper addresses this oversight by pioneering the examination of cybersecurity challenges presented by smart transformers. We introduce a comprehensive architecture and a well-formed taxonomy of cyber-physical attacks relevant to smart transformers. We present a detailed analysis of smart transformers' topology, identifying vulnerable components and illustrating the ramifications of cybersecurity breaches. Finally, we discuss potential future cybersecurity challenges facing smart transformers. Our findings underscore the need for integrating advanced cybersecurity measures into the development of online condition monitoring systems, safeguarding power transformers against evolving cyber threats while paving the way for more resilient power grids.

关键词： Power transformers Computer security Substations Monitoring Oil insulation Security Malware Cyberattack Smart grids Oils Cyber attack cybersecurity detection algorithm intrusion detection system online condition monitoring power transformer

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