با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
A hybrid data mining approach for anomaly detection and evaluation in residential buildings energy data
یک رویکرد داده کاوی ترکیبی برای تشخیص و ارزیابی ناهنجاری در داده های انرژی ساختمانهای مسکونی-2020
With the development in information technologies, today’s building energy consumption can be well monitored by the building energy management systems. However, in most real applications there is no clear definition of abnormal building energy consumption. To overcome this limitation, this work pro- poses a novel deep learning based unsupervised anomaly detection framework that includes recurrent neural networks and quantile regression. Moreover, this framework is able to produce a prediction inter- val to detect and evaluate abnormal building energy consumption. The framework has been applied to analyze the energy data collected from three different residential houses, and anomaly detection results are evaluated by the quantile regression range. The research results can provide promising solutions for building managers to detect abnormal energy performance, and is also valuable to assess the level of anomalies and spot opportunities in energy conservation.
Keywords: Deep learning | Quantile regression | Anomaly detection | Building energy management
Image anomaly detection for IoT equipment based on deep learning
تشخیص ناهنجاری تصویر برای تجهیزات اینترنت اشیا بر اساس یادگیری عمیق-2019
Intelligent power grid systems is the trend of power development, since traditional methods of manually monitoring power equipment have been unable to meet the requirements of power systems. When an abnormal situation occurs in the operating environment, most monitoring devices cannot be quickly and accurately identified, which may have serious consequences. Aiming at the above problems, in this paper, we propose an anomaly detection algorithm for the monitoring environment of power IoT equipment operating environment based on deep learning from the perspective of personnel identification and fire smoke detection. The multi-stream CNN-based remote monitoring image personnel detection method and the deep convolutional neural network-based fire smoke detection method have achieved good results in personnel identification and fire smoke detection in the power equipment operating environment monitoring image, respectively. This provides a reference for monitoring image anomaly detection.
Keywords: Operating environment monitoring | Image anomaly detection | Deep learning
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
حمله و تشخیص ناهنجاری در سنسورهای IoT در سایت های IoT با استفاده از روشهای یادگیری ماشین-2019
Attack and anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every do- main, threats and attacks in these infrastructures are also growing commensurately. De- nial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
Keywords: Internet of Things (IoT) | Machine Learning | Cybersecurity | Anomaly detection
Deep learning approach for sustainable WWTP operation: A case study on data-driven influent conditions monitoring
رویکرد یادگیری عمیق برای عملکرد پایدار WWTP: مطالعه موردی در زمینه نظارت بر شرایط تأثیرگذار محور داده-2019
Wastewater treatment plants (WWTPs) are sustainable solutions to water scarcity. As initial conditions offered to WWTPs, influent conditions (ICs) affect treatment units states, ongoing processes mechanisms, and product qualities. Anomalies in ICs, often raised by abnormal events, need to be monitored and detected promptly to improve system resilience and provide smart environments. This paper proposed and verified data-driven anomaly detection approaches based on deep learning methods and clustering algorithms. Combining both the ability to capture temporal auto-correlation features among multivariate time series from recurrent neural networks (RNNs), and the function to delineate complex distributions from restricted Boltzmann machines (RBM), RNN-RBM models were employed and connected with various classifiers for anomaly detection. The effectiveness of RNN based, RBM based, RNN-RBM based, or standalone individual detectors, including expectation maximization clustering, K-means clustering, mean-shift clustering, one-class support vector machine (OCSVM), spectral clustering, and agglomerative clustering algorithms were evaluated by importing seven years ICs data from a coastal municipal WWTP where more than 150 abnormal events occurred. Results demonstrated that RNN-RBM-based OCSVM approach outperformed all other scenarios with an area under the curve value up to 0.98, which validated the superiority in feature extraction by RNN-RBM, and the robustness in multivariate nonlinear kernels by OCSVM. The model was flexible for not requiring assumptions on data distribution, and could be shared and transferred among environmental data scientists.
Keywords: Wastewater treatment plant | Influent conditions monitoring | Machine learning | Unsupervised deep learning
Deep learning only by normal brain PET identify unheralded brain anomalies
یادگیری عمیق فقط با PET مغز نرمال ناهنجاریهای مغزی هدایت نشده را شناسایی می کند-2019
Background: Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and realworld data. We aimed to develop a model trained only by normal brain PET data with an unsupervised manner to identify an abnormality in various disorders as imaging data of the clinical routine. Methods: Using variational autoencoder, a type of unsupervised learning, Abnormality Scorewas defined as how far a given brain image is from the normal data. The model was applied to FDG PET data of Alzheimers disease (AD) andmild cognitive impairment (MCI) and clinical routine FDG PET data for assessing behavioral abnormality and seizures. Accuracy was measured by the area under curve (AUC) of receiver-operating-characteristic (ROC) curve.We investigated whether deep learning has additional benefits with experts visual interpretation to identify abnormal patterns. Findings: The AUC of the ROC curve for differentiating AD was 0.90. The changes in cognitive scores frombaseline to 2-year follow-up were significantly correlated with Abnormality Score at baseline. The AUC of the ROC curve for discriminating patients with various disorders from controls was 0.74. Experts visual interpretation was helped by the deep learning model to identify abnormal patterns in 60% of cases initially not identified without the model. Interpretation:We suggest that deep learning model trained only by normal data was applicable for identifying wide-range of abnormalities in brain diseases, even uncommon ones, proposing its possible use for interpreting real-world clinical data.
Keywords: PET | Deep learning | Variational autoencoder | Alzheimer | Anomaly detection
Motif-based association rule mining and clustering technique for determining energy usage patterns for smart meter data
روش استخراج و مجموعه خوشه بندی قانون مبتنی بر موتیف برای تعیین الگوهای مصرف انرژی برای داده های کنتور هوشمند-2019
Nowadays, smart energy meters are being used to record periodic electricity consumption. The real time data produced by smart meters provide the detailed information about the electricity usage of a particular consumer. In this paper, we propose a motif-based association rule mining and clustering technique for determining the energy usage patterns for smart meter data. The association rules of motifs within a specific time window characterizes behaviors of energy consumer. In particular, we focus on an extraction of the temporal information of the smart meter. The process is based on the unique combination of Symbolic Aggregate approximation (SAX), temporal motif discovery and association rule mining to detect the expected and unexpected patterns robustly. Experiments on real world smart meter datasets justify that the proposed model discovers the useful routine behavior of electricity energy consumers, which are helpful for electricity utility experts. Further, in this paper, clustering on the motifs is performed which gives the different consumption behavior of consumers on different days which can help distribution network operator (DNO) for electricity network modeling and management. In future, we can form motif-based signature using the proposed approach for different applications such as anomaly detection and dynamic detection of operating patterns.
Keywords: Smart Meter | Association rule | Data analytics | Temporal data mining | Clustering Motif
Design of machine learning models with domain experts for automated sensor selection for energy fault detection
طراحی مدلهای یادگیری ماشینی با کارشناسان دامنه برای انتخاب سنسور خودکار برای تشخیص خطای انرژی-2019
Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance through fault detection and system health monitoring. To lower cost barriers to widespread deployment, a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system to create ‘virtual sensors’, and applies statistical and mathematical methods to reduce the time required for manual configurations. The approach combines sensor data points with encoded expert knowledge that is generic to the application system but independent of a particular deployment, thereby reducing the need to tailor to individual deployments. This paper not only presents a method that detects faults from measured energy data, but also (1) describes an engagement method with experts in the energy system domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault detection model. Demonstration on a commercial building chiller plant shows that only a small number of virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools. The goal is to design with domain experts an energy monitoring system that can be configured once and then widely deployed with little additional cost or effort
Keywords: Machine learning | Domain knowledge | Time series | Fault detection | Anomaly detection | Energy savings | Energy efficiency
Multiple ellipse fitting of densely connected contours
اتصالات بیضی متعدد از کانتورهای متراکم متصل شده-2019
Multiple ellipse fitting is challenging and at the same time essential as it has a variety of applications in biology, chemistry, and nanotechnology. Accurate, effective, and reliable ap- proach for the fitting problem has been always desirable. In this paper, we address a cate- gory of multiple ellipse fitting problem which fits densely connected contours. We propose a framework rather than design an algorithm for the problem. The framework streamlines five processes which include: sorting the contour points, doing ellipse fitting in sliding windows, detecting the context anomaly, performing clustering, and obtaining multiple el- lipses through second ellipse fitting. The framework is evaluated in a real-world applica- tion of handprint identification and various synthetic datasets. Experimental results show that the framework can extract multiple ellipses from contours with satisfactory accuracy and efficiency.
Keywords: Multiple ellipse fitting | Sliding window | Anomaly detection | Cyclically ordered set | Pattern recognition
LSC: Online auto-update smart contracts for fortifying blockchain-based log systems
LSC: به روز رسانی خودکار قراردادهای هوشمند برای تقویت سیستم های ورود به سیستم بلاکچین-2019
Smart contracts allow verifiable operations to be executed in blockchains, bringing new possibilities for trust establishment in trustless scenarios. However, smart contracts are cumbersome when used as security mechanisms in security scenarios due to two reasons: they have limited power and are inert to changes . In order to mitigate the two problems of employed smart contracts, we propose LSC, a framework for online auto-update smart contracts in blockchain-based log systems, to en- able self-adaptive log anomaly detection via smart contracts. Time-varying log anomaly de- tection patterns are extracted by self-adaptive machine learning log anomaly analysis and are continuously fed to the contracts. The framework allows smart contracts to be auto- matically updated to express the patterns in low-cost ways. The anomaly detection strate- gies for audit log systems are shared and collaboratively enforced amongst network nodes to defend against targeted detection evasion. We provide a plain prototype as a proof of the feasibility and efficiency of LSC in log system.
Keywords: Smart contracts | Anomaly detectiony | Blockchain security | Security dynamics | Concept drift
Application of machine learning to accidents detection at directional drilling
کاربرد یادگیری ماشین در تشخیص تصادفات در حفاری جهت دار-2019
We present a data-driven algorithm and mathematical model for anomaly alarming at di- rectional drilling. The algorithm is based on machine learning. It compares the real-time drilling telemetry with one corresponding to past accidents and analyses the level of similar- ity. The model performs a time-series comparison using aggregated statistics and Gradient Boosting classication. It is trained on historical data containing the drilling telemetry of 80 wells drilled within 19 oilelds. The model can detect an anomaly and identify its type by comparing the real-time measurements while drilling with the ones from the database of past accidents. Validation tests show that our algorithm identies half of the anomalies with about 0:53 false alarms per day on average. The model performance ensures sucient time and cost savings as it enables partial prevention of the failures and accidents at the well construction
Keywords: machine learning | anomaly detection | directional drilling | classication | measurements while drilling