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تعداد مقالات یافته شده: 24
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1 Modeling and long-term forecasting demand in spare parts logistics businesses
مدلسازی و پیش بینی بلند - مدت تقاضا در کسب و کارهای لوازم بخشهای یدکی-2018
In order to provide high service levels, companies competing in the electronics manufacturing sector need to ensure the availability of spare parts for repair and maintenance operations. This paper examines the purchase life-cycles of electronic spare parts and presents a new way of modeling and forecasting spare part demand for electronic commodities in the spare parts logistics services. The presented modeling methodology is founded on the assumption that the purchase life-cycles of spare parts can be described by a curve with short term fluctuations around it. For this purpose, a flexible Demand Model Function is introduced. The proposed forecasting method uses a knowledge discovery-based approach that is built upon the combined application of analytic and soft computational techniques and is able to indicate the turning points of the purchase life-cycle curve. The novelty lies in the fact that the model function has certain characteristics which support describing and interpreting the demand trend as a function of time. The application of our methodology is mainly advantageous in long-term forecasting, it can be especially useful in supporting purchase planning decisions in the ramp-up and declining phases of purchase life-cycles of product specific spare parts. A demonstrative example is used to illustrate the applicability of the proposed methodology. Its forecasting capability is compared to those of some widely applied methods in business practice. From the results, the new method may be viewed as a viable alternative spare part demand forecasting technique in spare part logistics sector.
keywords: Spare part logistics |Electronic aftermarket services |Purchase life-cycle forecasting |Knowledge discovery |Clustering time series
مقاله انگلیسی
2 Data and knowledge mining with big data towards smart production
داده و دانش کاوی با داده های بزرگ به سوی تولید هوشمند-2018
Driven by the innovative improvement of information and communication technologies (ICTs) and their ap plications into manufacturing industry, the big data era in manufacturing is correspondingly arising, and the developing data mining techniques (DMTs) pave the way for pursuing the aims of smart production with the real-time, dynamic, self-adaptive and precise control. However, lots of factors in the ever-changing environment of manufacturing industry, such as, various of complex production processes, larger scale and uncertainties, more complicated constrains, coupling of operational performance, and so on, make production management face with more and more big challenges. The dynamic inflow of a large number of raw data which is collected from the physical manufacturing sites or generated in various related information systems, caused the heavy information overload problems. Indeed, most of traditional DMTs are not yet sufficient to process such big data for smart production management. Therefore, this paper reviews the development of DMTs in the big data era, and makes discussion on the applications of DMTs in production management, by selecting and analyzing the relevant papers since 2010. In the meantime, we point out limitations and put forward some suggestions about the smartness and further applications of DMTs used in production management.
Keywords: Big data , Data mining techniques (DMTs) , Production management , Smart manufacturing ,| Statistical analysis , Knowledge discovery
مقاله انگلیسی
3 داده و دانش کاوی با داده های بزرگ برای تولید هوشمند
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 45
مطابق با پیشرفت نوآورانه فناوری اطلاعات و ارتباطات (ICT) و کاربرد آنها در صنعت تولید، دوران بزرگ داده های تولیدی مطابق با انها است و تکنیک های داده کاوی (DMTs)، راه را برای دستیابی به اهداف تولید هوشمند با کنترل زمان واقعی، پویا، خود سازگار و دقیق فراهم می سازد. با این حال، بسیاری از عوامل در محیط همیشه در حال تغییر در صنعت تولید هستند، از جمله، فرآیندهای تولید پیچیده، مقیاس بزرگ و عدم اطمینان، محدودیت پیچیده تر، ترکیب عملکردی عملیات، و غیره، که تولید مدیریت را با چالش های بزرگی همراه می سازد. ورودی پویا تعداد زیادی از داده های خام که از مکان های تولید فیزیکی جمع آوری شده یا تولید شده است؛ در سیستم های مختلف مربوط به اطلاعات، موجب شد تا مشکلات سنگین اطلاعات بیش از حد فراهم شود. در واقع، بسیاری از DMT های سنتی هنوز به اندازه کافی برای پردازش داده های بزرگ در تولید مدیریت هوشمند نیستند. بنابراین، در این مقاله، توسعه DMT ها در دوران بزگی از داده ها را مورد بررسی قرار می دهیم و از سال 2010 با انتخاب و تجزیه و تحلیل مقالات مربوطه در مورد کاربرد DMT ها در مدیریت تولید بحث می کنیم. در عین حال، در این مقاله ما محدودیت ها را مطرح می کنیم و برخی از پیشنهادات را در مورد هوشمند بودن و کاربرد بیشتر DMT ها که در مدیریت تولید به کار می رود را ارائه می دهیم.
کلمات کلیدی: داده های بزرگ | تکنیک های داده کاوی (DMTs) | مدیریت تولید | تولید هوشمند | تجزیه و تحلیل آماری | کشف دانش
مقاله ترجمه شده
4 تحقیقات مهمان نوازی: میراث ها و آینده ها
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 14
یک گزارشی درمورد پیشرفت های تحقیقات مهمان نوازی درطی دو دهه گذشته ارائه می شود. نویسنده ازطریق ارائه مثالهای گویا، یک انعکاس شخصی درباره مسافرت دریایی انجام داده، کشف دانش و تکامل آنچه که با عنوان مطالعات مهمان نوازی به آن ارجاع می شود را ارئه می دهد. روی فعالیتهای دانشمندان در رشته مدیریت مهمان نوازی و فعالیتهای قرار گرفته در علوم اجتماعی گسترده تر بحث می شود. با پیشرفت سفرهای دریایی، واضح است که تلاشهای مجامع علمی منجر به برخی میراث ها شده است. بخش نظریه نتیجه گیری می کند که یک آینده ای به تحقیقات مهمان نوازی اشاره می کند که از تنوع فکری و روشهای خرد جمعی تقدیر می کنند.
مقاله ترجمه شده
5 Data mining and clustering in chemical process databases for monitoring and knowledge discovery
داده کاوی و خوشه بندی در پایگاه داده های فرایند شیمیایی برای نظارت و کشف دانش-2018
Modern chemical plants maintain large historical databases recording past sensor measurements which advanced process monitoring techniques analyze to help plant operators and engineers interpret the meaning of live trends in databases. However, many of the best process monitoring methods require data organized into groups before training is possible. In practice, such organization rarely exists and the time required to create classified training data is an obstacle to the use of advanced process monitoring strate gies. Data mining and knowledge discovery techniques drawn from computer science literature can help engineers find fault states in historical databases and group them together with little detailed knowledge of the process. This study evaluates how several data clustering and feature extraction techniques work together to reveal useful trends in industrial chemical process data. Two studies on an industrial scale separation tower and the Tennessee Eastman process simulation demonstrate data clustering and feature extraction effectively revealing significant process trends from high dimensional, multivariate data. Pro cess knowledge and supervised clustering metrics compare the cluster results against true labels in the data to compare performance of different combinations of dimensionality reduction and data clustering approaches.
Keywords: Data mining ، Data clustering ، Dimensionality reduction ، Knowledge discovery
مقاله انگلیسی
6 Data and knowledge mining with big data towards smart production
استخراج داده ها و دانش با داده های بزرگ به سوی تولید هوشمند-2018
Driven by the innovative improvement of information and communication technologies (ICTs) and their ap plications into manufacturing industry, the big data era in manufacturing is correspondingly arising, and the developing data mining techniques (DMTs) pave the way for pursuing the aims of smart production with the real-time, dynamic, self-adaptive and precise control. However, lots of factors in the ever-changing environment of manufacturing industry, such as, various of complex production processes, larger scale and uncertainties, more complicated constrains, coupling of operational performance, and so on, make production management face with more and more big challenges. The dynamic inflow of a large number of raw data which is collected from the physical manufacturing sites or generated in various related information systems, caused the heavy information overload problems. Indeed, most of traditional DMTs are not yet sufficient to process such big data for smart production management. Therefore, this paper reviews the development of DMTs in the big data era, and makes discussion on the applications of DMTs in production management, by selecting and analyzing the relevant papers since 2010. In the meantime, we point out limitations and put forward some suggestions about the smartness and further applications of DMTs used in production management.
Keywords: Big data ، Data mining techniques (DMTs) ، Production management ، Smart manufacturing ، Statistical analysis ، Knowledge discovery
مقاله انگلیسی
7 ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data
ViSiBiD: مدل های یادگیری برای کشف زودرس و پیش بینی زمان واقعی از حوادث بالینی شدید با استفاده از علائم حیاتی به عنوان داده های بزرگ-2017
The advance in wearable and wireless sensors technology have made it possible to monitor multiple vital signs (e.g. heart rate, blood pressure) of a patient anytime, anywhere. Vital signs are an essential part of daily monitoring and disease prevention. When multiple vital sign data from many patients are accumulated for a long period they evolve into big data. The objective of this study is to build a prognostic model, ViSiBiD, that can accurately identify dangerous clinical events of a home-monitoring patient in advance using knowledge learned from the patterns of multiple vital signs from a large number of similar patients. We developed an innovative technique that amalgamates existing data mining methods with smartly extracted features from vital sign correlations, and demonstrated its effectiveness on cloud platforms through comparative evaluations that showed its potential to become a new tool for predictive healthcare. Four clinical events are identified from 4893 patient records in publicly available databases where six bio-signals deviate from normality and different features are extracted prior to 1–2 h from 10 to 30 min observed data of those events. Known data mining algorithms along with some MapReduce implementations have been used for learning on a cloud platform. The best accuracy (95.85%) was obtained through a Random Forest classifier using all features. The encouraging learning performance using hybrid feature space proves the existence of discriminatory patterns in vital sign big data can identify severe clinical danger well ahead of time.
Keywords: Big data | Vital sign | Cloud computing | Correlations | Knowledge discovery | Data mining
مقاله انگلیسی
8 Semantic recognition of workpiece using computer vision for shape feature extraction and classification based on learning databases
تشخیص معنایی قطعه کار با استفاده از بینایی ماشین برای استخراج و طبقه بندی ویژگی اشکال بر اساس پایگاه داده های یادگیری-2017
In this study, the Knowledge Discovery Database (KDD) was used to aid the computer to visualize, recognize and classify workpieces automatically through intelligent identifica tion. In order to acquire initial statistical shape features, smooth and segment workpieces images were accurately taken and the shape features were stored into a database. Rough Set and Relevancy Analysis was used to reduce the shape features in order to generate the minimum characteristic vector. Finally, the generated classification rules were used to guide semantic recognition as prior knowledge. The algorithm is verified on four different types of workpieces, and the results showed that the obtained shape features are suitable for object classification and recognition semantically. This paper provides a new technique of image processing of machine vision and has great significance in the future study.
Keywords: Computer vision | Semantic recognition | Shape features | Knowledge discovery database (KDD)
مقاله انگلیسی
9 Discovering partial periodic-frequent patterns in a transactional database
کشف الگوهای تناوبی مکرر جزئی در یک پایگاه داده های تراکنشی-2017
Time and frequency are two important dimensions to determine the interestingness of a pattern in a database. Periodic-frequent patterns are an important class of regularities that exist in a database with respect to these two dimensions. Current studies on periodic-frequent pattern mining have focused on discovering full periodic-frequent patterns, i.e., finding all frequent patterns that have exhibited complete cyclic repetitions in a database. However, partial periodic-frequent patterns are more common due to the imperfect nature of real-world. This paper proposes a flexible and generic model to find partial periodic frequent patterns. A new interesting measure, periodic-ratio, has been introduced to determine the peri odic interestingness of a frequent pattern by taking into account its proportion of cyclic repetitions in a database. The proposed patterns do not satisfy the anti-monotonic property. A novel pruning technique has been introduced to reduce the search space effectively. A pattern-growth algorithm to find all partial periodic-frequent patterns has also been presented in this paper. Experimental results demonstrate that the proposed model can discover useful information, and the algorithm is efficient.
Keywords: Data mining | Knowledge discovery in databases | Pattern mining | Partial periodicity | Algorithms
مقاله انگلیسی
10 Knowledge Discovery for Smart Grid Operation, Control, and Situation Awareness: A Big Data Visualization Platform
کشف دانش برای هوش مصنوعی عملیات، کنترل و وضعیت هوشمند: یک پلت فرم تجسم داده بزرگ-2016
In this paper, a big data visualization platform is designed to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. The spawn of smart sensors at both grid side and customer side can provide large volume of heterogeneous data that collect information in all time spectrums. Extracting useful knowledge from this bigdata poll is still challenging. In this paper, the Apache Spark, an open source cluster computing framework, is used to process the big-data to effectively discover the hidden knowledge. A highspeed communication architecture utilizing the Open System Interconnection (OSI) model is designed to transmit the data to a visualization platform. This visualization platform uses Google Earth, a global geographic information system (GIS) to link the geological information with the SG knowledge and visualize the information in user defined fashion. The University of Denver’s campus grid is used as a SG test bench and several demonstrations are presented for the proposed platform.
Index terms: Big data | knowledge discovery | smart sensor | Apache Spark | geographic information system | parallel computation
مقاله انگلیسی
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