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نتیجه جستجو - Time series

تعداد مقالات یافته شده: 106
ردیف عنوان نوع
1 Parallel Time-Delay Reservoir Computing With Quantum Dot Lasers
محاسبات مخزن تاخیر زمانی موازی با لیزرهای کوانتومی-2022
A semiconductor laser with optical feedback and optical injection is an appealing scheme to construct the time-delay reservoir computing (TDRC) networks. Quantum dot (QD) lasers are compatible to the silicon platform, and hence is helpful to develop fully on-chip TDRCs. This work theoretically demonstrates a parallel TDRC based on a Fabry-Perot QD laser with multiple longitudinal modes. These modes act as connected physical neurons, which process the input signal in parallel. The interaction strength of the modes is characterized by the cross-gain saturation effect. We show that the neuron interaction strength affects the performance of various benchmark tasks, including the memory capacity, time series prediction, nonlinear channel equalization, and spoken digit recognition. In comparison with the one-channel TDRC with the same number of nodes, the parallel TDRC runs faster and its performance is improved on multiple benchmark tasks.
Index Terms: Reservoir computing | quantum dot lasers | optical neural network | optical feedback | optical injection
مقاله انگلیسی
2 A Quantum-Inspired Classifier for Early Web Bot Detection
یک طبقه بندی الهام گرفته از کوانتومی برای تشخیص زودهنگام ربات وب-2022
This paper introduces a novel approach, inspired by the principles of Quantum Computing, to address web bot detection in terms of real-time classification of an incoming data stream of HTTP request headers, in order to ensure the shortest decision time with the highest accuracy. The proposed approach exploits the analogy between the intrinsic correlation of two or more particles and the dependence of each HTTP request on the preceding ones. Starting from the a-posteriori probability of each request to belong to a particular class, it is possible to assign a Qubit state representing a combination of the aforementioned probabilities for all available observations of the time series. By leveraging the underlying mathematical details of superposition and entanglement on specific subsequences, it is possible to devise a measure of membership to each class, thus enabling the system to take a reliable decision when a sufficient level of confidence is met or to continue with additional observations. The results reported in this paper objectively show the effectiveness of our quantum-inspired algorithm which outperforms other state-of-the-art approaches, including our own one based on the Sequential Probability Ratio Test.
Index Terms— Quantum-inspired computing | bot detection | sequential classification | early decision | multinomial classification | multivariate sequence classification.
مقاله انگلیسی
3 Evaluation of printable character-based malicious PE file-detection method
ارزیابی روش تشخیص فایل PE مخرب مبتنی بر کاراکتر قابل چاپ-2022
Printable characters extracted from portable executable (PE) files are a common surface analysis feature. String extraction is a supplemental feature for malware analysis. Recent developments in natural language processing techniques have enabled the rapid detection of malicious PE files. Previously, we proposed a method for detecting malicious PE files using printable characters using two language models for feature extraction and machine-learning. In this study, we evaluated the method using the latest FFRI dataset consisting of 400,000 benign and 400,000 malicious samples between 2019 and 2021. To the best of our knowledge, this is the first study to consider the time series of both malicious and benign samples. According to the results, specific tokens in the printable characters were effective in detecting the latest malicious PE files. The most practical combination was of the Doc2vec and multilayer perceptron, which achieved an F1 score of 0.981. Each run time showed an almost linear increase with increasing dataset size.
Keywords: Malware | Machine learning | Natural language processing
مقاله انگلیسی
4 Modelling corporate bank accounts
مدل سازی حساب های بانکی شرکت ها-2021
We discuss the modelling of corporate bank accounts using a proprietary dataset. We thus offer a principled treatment of a genuine industrial problem. The corporate bank accounts in our study constitute spare, irregularly-spaced time series that may take both positive and negative values. We thus builds on previous models where the underlying is real-valued. We describe an intra-monthly effect identified by practitioners whereby account uncertainty is typically lowest at the beginning and end of each month and highest in the middle. However, our theory also allows for the opposite effect to occur. In-sample applications demonstrate the statistical significance of the hypothesized monthly effect. Out-of-sample forecasting applications offer a 9% improvement compared to a standard SARIMA approach.
keywords: حساب های بانکی شرکت | فناوری | پیش بینی برنامه های کاربردی | یادگیری ماشین | Corporate bank accounts | Fin Tech | Forecasting applications | Machine learning
مقاله انگلیسی
5 A Requiem for ‘‘Blame It on Beijing” interpreting rotating global current account surpluses
مرثیه ای برای «سرزنش آن به گردن پکن» که در حال تفسیر جهانی در حال چرخش است مازاد حساب جاری-2021
Global current account imbalances have reappeared, although the extent and distribution of these imbalances are noticeably different from those experienced in the middle of the last decade. What does that recurrence mean for our understanding of the origin and nature of such imbalances? Will imbalances persist over time? Informed by empirical estimates of the determinants of current account imbalances encompassing the period after the global recession, we find that – as before – the observable manifestations of the factors driving the global saving glut have limited explanatory power for the time series variation in imbalances. Fiscal factors determine imbalances, and have accounted for a noticeable share of the recent variation in imbalances, including in the U.S. and Germany. For advanced economies, the financial component of the current account has been playing an increasing role in determining the movements of the account. Examining observable policy actions, it is clear that net official flows have been associated with some share of imbalances, although tracing out the motivations for intervention is difficult. Looking forward, it is clear that policy can influence global imbalances, although some component of the U.S. deficit will likely remain given the U.S. role in generating safe assets.
keywords: تعادل حساب جاری | دارایی های خارجی خالص | صرفه جویی | مروریسم | محافظت از خود | دارایی های ایمن | fi flows | Current account balance | Net foreign assets | Saving glut | Mercantilism | Self-protection | Safe assets | Official flows
مقاله انگلیسی
6 Nonlinear analysis and active management of production-distribution in nonlinear supply chain model using sliding mode control theory
تحلیل غیرخطی و مدیریت فعال تولید-توزیع در مدل غیر خطی زنجیره تامین با استفاده از تئوری کنترل حالت کشویی-2021
This paper deals with system dynamics approach for dynamical behaviors and control synthesis of supply chain system by utilizing three-stage production-distribution model. The presented approach offers systematic tools for determining fundamental relationships between multi-echelons in the supply chain dynamics by using eigenvalues, bifurcation, and time history investigation. By exploring system dynamics on time series analysis, it is found that system performance has suffered severely from the bullwhip effect under impacts of model uncertainties and perturbed demand. The novel fractional-order sliding mode control algorithm has been presented based on adaptation mechanism, ensuring that the shipment flows are robustly stable in supply chain networks against disruptions. This is a smarter way of getting sufficient strength to sustain existing competitive market for mitigating the risks and improving the supply chain performance. The system stability has been thoroughly analyzed by using Routh-Hurwitz criterion and Lyapunov theory. Extensive numerical simulations have been conducted to obtain insights into the system behaviors and to validate effectiveness of active control policies by matching the shipment sent to customer demand, ensuring supply chains resilience. Finally, it is found that the presented approach can help decision-makers develop more efficient supply chain management system against severe market disruptions.
Keywords: System dynamics | Supply chain management | Production-distribution model | Fractional order | Sliding mode control | Adaptive law
مقاله انگلیسی
7 Forecasting third-party mobile payments with implications for customer flow prediction
پیش بینی پرداخت های تلفن همراه شخص ثالث با پیامدهای پیش بینی جریان مشتری-2020
Forecasting customer flow is key for retailers in making daily operational decisions, but small retailers often lack the resources to obtain such forecasts. Rather than forecasting stores’ total customer flows, this research utilizes emerging third-party mobile payment data to provide participating stores with a value-added service by forecasting their share of daily customer flows. These customer transactions using mobile payments can then be utilized further to derive retailers’ total customer flows indirectly, thereby overcoming the constraints that small retailers face. We propose a third-party mobile-paymentplatform centered daily mobile payments forecasting solution based on an extension of the newly-developed Gradient Boosting Regression Tree (GBRT) method which can generate multi-step forecasts for many stores concurrently. Using empirical forecasting experiments with thousands of time series, we show that GBRT, together with a strategy for multi-period-ahead forecasting, provides more accurate forecasts than established benchmarks. Pooling data from the platform across stores leads to benefits relative to analyzing the data individually, thus demonstrating the value of this machine learning application.
Keywords: Analytics | Big data | Customer flow forecasting | Machine learning | Forecasting many time series | Multi-step-ahead forecasting strategy
مقاله انگلیسی
8 The effects of Chile’s 2005 traffic law reform and in-country socioeconomic differences on road traffic deaths among children aged 0-14 years: A 12-year interrupted time series analysis
اثرات اصلاح قانون راهنمایی و رانندگی در سال 2005 شیلی و اختلافات اقتصادی و اجتماعی درون کشور در مورد مرگ و میر در جاده های کودکان در سن 0-14 سال: تجزیه و تحلیل قطع 12 ساله سری های زمانی -2020
Objectives: This study assessed the effect of Chile’s 2005 traffic law reform (TLR) on the rates of road traffic deaths (RTD) in children aged 0–14 years, adjusting for socioeconomic differences among the regions of the country. Methods: Free-access sources of official and national information provided the data for every year of the study period (2002–2013) and for each of the country’s 13 upper administrative divisions with respect to RTD in child pedestrians and RTD in child passengers (dependent variables), and the following control variables: the number of road traffic tickets processed, investment in road infrastructure, poverty, income inequality, insufficient education, unemployment, population aged 0–14 years, and prevalence of alcohol consumption in the general population. Interrupted time series analyses (level and slope change impact model), using generalized estimating equation methods, were conducted to assess the impact of the TLR (independent variable) on the dependents variables. Results: There was a significant interaction between time and Chile’s 2005 TLR for a reduction in child pedestrians (incidence rate ratio [IRR] 0.87, 95% confidence interval [CI] 0.79-0.96) and passengers RTD (IRR for interaction 0.80, 95% CI 0.67-0.96) trends. In addition, in child pedestrians, RTD rates were affected by poverty (IRR 1.04, 95% CI 1.02–1.05), income inequality (IRR 1.02, 95% CI 1.00–1.04), and unemployment (IRR 0.94, 95% CI 0.90-0.98), whereas in the case of child passengers, poverty (IRR 1.05, 95% CI 1.01–1.08) and income inequality (IRR 0.93, 95% CI 0.91-0.95) were significant. Conclusions: Large-scale legislative actions can be effective road safety measures if they are aimed at promoting behavioral change in developing countries, improving the safety of children on the road. Additionally, regional socioeconomic differences are associated with higher RTD rates in this population, making this an argument in favor of road safety policies that consider these inequalities. The number of road traffic tickets processed and the investment in road infrastructure were not significant.
Keywords: Safety management | Child | Traffic accidents | Mortality | Socioeconomic factors
مقاله انگلیسی
9 Effectiveness of implementing the criminal administrative punishment law of drunk driving in China: An interrupted time series analysis, 2004-2017
اثربخشی اجرای قانون مجازات اداری کیفری رانندگی مست در چین: تجزیه و تحلیل سری های زمانی قطع شده ، 2004-2017-2020
In 2011, a more severe drunk driving law was implemented in China, which criminalized driving under the influence of alcohol for the first time and increased penalties for drunk driving. The present study aimed to assess effectiveness of the drunk driving law in China in reducing traffic crashes, injuries, and mortality. Data used in this study was obtained from the Traffic Management Bureau of the Ministry of Public Security of the People’s Republic of China. An interrupted time series analysis was conducted to analyze annual data from 2004 to 2017, including the number of road traffic crashes, deaths, and injuries caused by drunk driving in China. The average annual incidences of crashes, mortality, and injuries have decreased after the promulgation of drunk driving law in 2011. In the post-intervention period, the increased slope for crashes, mortality and injury rates were, respectively, -0.140 to -0.006, -0.052 to -0.005 and -0.150 to -0.008, indicating a weaker downward trend of dependent variables. The more stringent drunk driving law is not as effective as expected. Drunk driving is still a severe traffic safety problem to be addressed in China. Both legislation and other prevention programs should be adopted to reduce road traffic injuries caused by drunk driving in China.
Keywords: Drunk driving | Interrupted time series analysis | Road traffic law | Injury | Evaluation | China
مقاله انگلیسی
10 Refined composite multivariate multiscale symbolic dynamic entropy and its application to fault diagnosis of rotating machine
آنتروپی پویای نمادین چند متغیره کامپوزیت تصفیه شده و کاربرد آن در تشخیص خطای ماشین چرخشی-2020
Accurate and efficient identification of various fault categories, especially for the big data and multisensory system, is a challenge in rotating machinery fault diagnosis. For the diagnosis problems with massive multivariate data, extracting discriminative and stable features with high efficiency is the significant step. This paper proposes a novel feature extraction method, called Refined Composite multivariate Multiscale Symbolic Dynamic Entropy (RCmvMSDE), based on the refined composite analysis and multivariate multiscale symbolic dynamic entropy. Specifically, multivariate multiscale symbolic dynamic entropy can capture more identification information from multiple sensors with superior computational efficiency, while refine composite analysis guarantees its stability. The abilities of the proposed method to measure the complexity of multivariate time series and identify the signals with different components are discussed based on adequate simulation analysis. Further, to verify the effectiveness of the proposed method on fault diagnosis tasks, a centrifugal pump dataset under constant speed condition and a ball bearing dataset under time-varying speed condition are applied. Compared with the existing methods, the proposed method improves the classification accuracy and F-score to 99.81% and 0.9981, respectively. Meanwhile, the proposed method saves at least half of the computational time. The result shows that the proposed method is effective to improve the efficiency and classification accuracy dealing with the massive multivariate signals.
Keywords: Multivariate multiscale symbolic dynamic | entropy | Random forest | Time-varying speed conditions | Fault diagnosis
مقاله انگلیسی
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