A dynamic classification unit for online segmentation of big data via small data buffers
واحد طبقه بندی پویا برای تقسیم آنلاین داده های بزرگ از طریق بافر داده های کوچک-2020
In many segmentation processes, we assign new cases according to a model that was built on the basis of past cases. As long as the new cases are “similar enough” to the past cases, segmentation proceeds normally. However, when a new case is substantially different from the known cases, a reexamination of the previously created segments is required. The reexamination may result in the creation of new segments or in the updating of the existing ones. In this paper, we assume that in big and dynamic data environments it is not possible to reexamine all past data and, therefore, we suggest using small groups of selected cases, stored in small data buffers, as an alternative to the collection of all past data. We present an incremental dynamic classifier that supports real-time unsupervised segmentation in big and dynamic data environments. In order to reduce the computational effort of unsupervised clustering in such environments, the suggested model performs calculations only on the relevant data buffers that store the relevant representative cases. In addition, the suggested model can serve as a dynamic classification unit (DCU) that can act as an autonomous agent, as well as collaborate with other DCUs. The evaluation is presented by comparing three approaches: static, dynamic, and incremental dynamic.
Keywords: Incremental dynamic classifier | Dynamic segmentation | Incremental data analysis | Cluster analysis | Classification | Big data
A real-time blended energy management strategy of plug-in hybrid electric vehicles considering driving conditions
یک استراتژی مدیریت انرژی ترکیبی از زمان واقعی خودروهای برقی پلاگین با توجه به شرایط رانندگی-2020
In this study, a blended energy management strategy considering influences of driving conditions is proposed to improve the fuel economy of plug-in hybrid electric vehicles. To attain it, dynamic programming is firstly applied to solve and quantify influences of different driving conditions and driving distances. Then, the driving condition is identified by the K-means clustering algorithm in real time with the help of Global Positioning System and Geographical Information System. A blended energy management strategy is proposed to achieve the real-time energy allocation of the powertrain with incorporation of the identified driving conditions and the extracted rules, which includes the engine starting scheme, gear shifting schedule and torque distribution strategy. Simulation results reveal that the proposed strategy can effectively adapt to different driving conditions with the dramatic improvement of fuel economy and the decrement of calculation intensity and highlight the feasibility of real-time implementation
Keywords: Plug-in hybrid electric vehicles | Energy management strategy | Global optimization | Driving condition | Equivalent driving distance coefficient
Zero-net energy management for the monitoring and control of dynamically-partitioned smart water systems
مدیریت انرژی صفر خالص برای نظارت و کنترل سیستم های اب هوشمند تقسیم شده -2020
The optimal and sustainable management of water distribution systems still represent an arduous task. In many instances, especially in aging water net-works, pressure management is imperative for reducing breakages and leakages. Therefore, optimal District Metered Areas represent an effective solution to decreasing the overall energy input without performance compromise. Within this context, this paper proposes a novel adaptive management framework for water distribution systems by reconfiguring the original network layout into (dynamic) district metered areas. It utilises a multiscale clustering algorithm to schedule district aggregation/desegregation, whilst delivering energy and supply management goals. The resulting framework was tested in a water utility network for the simultaneously production of energy during the day (by means of the installation of micro-hydropower systems) and for the reduction of water leakage during the night. From computational viewpoint, this was found to significantly reduce the time and complexity during the clustering and the dividing phase. In addition, in this case, a recovered energy potential of 19 MWh per year and leakage reduction of up to 16% was found. The addition of pump-as-turbines was also found to reduce investment and maintenance costs, giving improved reliability to the monitoring stations. The financial analyses to define the optimal period in which to invest also showed the economic feasibility of the proposed solution, which assures, in the analysed case study, a positive annual net income in just five years. This study demonstrates that the combined optimisation, energy recovery and creation of optimized multiple-task district stations lead to an efficient, resilient, sustainable, and low-cost management strategy for water distribution networks.
Keywords: Water distribution systems | Micro-hydropower systems | Sustainable and smart cities | Water-energy nexus | Water leakage reduction | Financial return-on-investment
A framework for extracting urban functional regions based on multi prototype word embeddings using points-of-interest data
چارچوبی برای استخراج مناطق عملکردی شهری بر اساس تعبیه چند کلمه نمونه اولیه با استفاده از داده های مورد علاقه-2020
Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geodata. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a centercontext pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed.
Keywords: Urban functional regions | Word embeddings | Points-of-interest | Spatial clusters
Online energy management strategy of fuel cell hybrid electric vehicles based on rule learning
استراتژی مدیریت انرژی آنلاین از وسایل نقلیه برقی هیبریدی سلول سوختی بر اساس یادگیری قانون-2020
In this paper, a rule learning based energy management strategy is proposed to achieve preferable energy consumption economy for fuel cell hybrid electric vehicles. Firstly, the optimal control sequence of fuel cell power and the state of charge trajectory of lithium-ion battery pack during driving are derived offline by the Pontryagin’s minimum principle. Next, the K-means algorithm is employed to hierarchically cluster the optimal solution into the simplified data set. Then, the repeated incremental pruning to produce error reduction algorithm, as a propositional rule learning strategy, is leveraged to learn and classify the underlying rules. Finally, the multiple linear regression algorithm is applied to fit the abstracted parameters of generated rule set. Simulation results highlight that the proposed strategy can achieve more than 95% savings of energy consumption economy, solved by Pontryagin’s minimum principle, with less calculation intensity and without dependence on prior driving conditions, thereby manifesting the feasibility of online application.
Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Hierarchical clustering | Rule learning
مروری سیستماتیک بر مدیریت منابع انسانی سبز : پیامدهایی برای پایداری اجتماعی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 65 - تعداد صفحات فایل doc فارسی: 55
این مقاله پیشرفتهای فعلی و شکافهای تحقیقاتی را در زمینه ادبیات مدیریت منابع انسانی سبز را مورد بررسی قرار میدهد و همچنین به بررسی شیوه های سبز در اینده و در مواجه با امور اجتماعی و الزامات پایداری یک سازمان میپردازد. با در نظر گرفتن افزایش اگاهی در مورد مفهوم سبز بودن و پایداری، یک بررسی سیستماتیک در زمینه ادبیات بصورت خاص انجام شده است که در آن از پایگاه داده های اسکوپوسScopus / و گوگل اسکالر/ Google Scholar استفاده شده است که منجر به انجام مجموعه ایی از 174 مطالعه در بین سالهای 1995 تا 2019 شد. در این مطالعه از نرم افزار NVivo Plus نسخه 12 برای پردازش کمی و همچنین تجزیه و تحلیل کیفی داده ها استفاده شد. کدگذاری محتوا و تجزیه و تحلیل خوشه ای انجام شد، که نتایج آن سه خوشه مجزا بنامهای شیوه های مدیریت منابع انسانی سبز، رفتار سبز کارکنان در محل کار و ثبات سازمانی را به نمایش گذاشتند. تجزیه و تحلیل دستی بیشتر نشان داد پایداری اجتماعی بعنوان یکی از حداقل حوزه های فراتر از ارکان اقتصادی و زیست محیطی پایداری است. از اینرو، نویسندگان بصورت مفهومی یک مدل نظری ارایه دادند که نشان دهنده نقش میانجی گری رفتار سبز کارکنان در محل کار در ارتباط با شیوه های مدیریت منابع انسانی سبز و ثبات اجتماع سازمانهایی است که از رویکرد نظریه داده بنیاد استفاده میکنند. این مطالعه اخرین تحقیقات مرتبط به تحقیقات مدیریت منابع انسانی سبز را بمنظور ثبات اجتماعی پیش بینی نشده را در زمینه ثبات اجتماعی مورد بررسی قرار میدهد، که تا کنون مورد ارزیابی قرار گرفته اند. بر اساس محتوای کدگذاری، خوشه بندی، و تجزیه و حلیل های بیشتر، گزاره ها ، مسیرها و پیامدهایی نیز برای آینده در نظر گرفته شده اند.
واژگان کلیدی: شیوه های مدیریت منابع انسانی سبز | رفتار سبز کارکنان در محل کار | ثبات سازمانی | مدیریت منابع انسانی پایدار | ثبات اجتماعی | تجزیه و تحلیل محتوا.
|مقاله ترجمه شده|
The clustering algorithm for efficient energy management in mobile ad-hoc networks
الگوریتم خوشه بندی برای مدیریت انرژی کارآمد در شبکه های ad-hoc تلفن همراه-2020
MANET (Mobile Ad-hoc Network) consumes much energy due to their dynamic capabilities, complex- ities, constraints of design such as lack of a specified communication infrastructure, and their change over time. One strategy to reduce energy consumption is to optimize routing in these networks, which is, in turn, one of the most important challenges in these networks. In addition, optimal routing will in- crease the network lifetime and lead to its stability. Considering the high efficiency of clustering methods among the routing algorithms we present a new clustering method and considering good performance of Evolutionary Algorithms (EAs) in finding proper head clusters, we present a specific EA-based method named ICA (Imperialist Competitive Algorithm) via numerical coding. By thinking of specific conditions of a MANET and estimating the mobility direction of nodes, we prevent from additional reclusterings lead- ing to reducing the overload. We have evaluated our proposed method for: 1) accuracy (including the reproducibility, convergence and stability criteria) through three case studies with different numbers of nodes and ranges and 2) efficiency (by comparing to other methods). Moreover, we applied our proposed method to a case study (multi-robot system) with low velocity.
Keywords: ICA | MANET | Clustering | Energy consumption
Explosive, continuous and frustrated synchronization transition in spiking Hodgkin–Huxley neural networks: The role of topology and synaptic interaction
انتقال همزمان ، انفجاری ، مداوم و ناامید کننده در شبکه های عصبی هوچکین-هاکسلی اسپایک: نقش توپولوژی و تعامل سیناپسی-2020
Synchronization is an important collective phenomenon in interacting oscillatory agents. Many functional features of the brain are related to synchronization of neurons. The type of synchronization transition that may occur (explosive vs. continuous) has been the focus of intense attention in recent years, mostly in the context of phase oscillator models for which collective behavior is independent of the mean-value of natural frequency. However, synchronization properties of biologically-motivated neural models depend on the firing frequencies. In this study we report a systematic study of gammaband synchronization in spiking Hodgkin–Huxley neurons which interact via electrical or chemical synapses. We use various network models in order to define the connectivity matrix. We find that the underlying mechanisms and types of synchronization transitions in gamma-band differs from beta-band. In gamma-band, network regularity suppresses transition while randomness promotes a continuous transition. Heterogeneity in the underlying topology does not lead to any change in the order of transition, however, correlation between number of synapses and frequency of a neuron will lead to explosive synchronization in heterogeneous networks with electrical synapses. Furthermore, small-world networks modeling a fine balance between clustering and randomness (as in the cortex), lead to explosive synchronization with electrical synapses, but a smooth transition in the case of chemical synapses. We also find that hierarchical modular networks, such as the connectome, lead to frustrated transitions. We explain our results based on various properties of the network, paying particular attention to the competition between clustering and long-range synapses.
Keywords: Synchronization | Hodgkin–Huxley neuron | Phase transition | Electrical and chemical synapses | Complex networks
A decision support system using hybrid AI based on multi-image quality model and its application in color design
یک سیستم پشتیبانی تصمیم گیری با استفاده از هوش مصنوعی ترکیبی مبتنی بر مدل کیفیت چند تصویر و کاربرد آن در طراحی رنگ-2020
The product-color image conveys consumers’ color demands through emotion cognition. In this paper, a decision support system is proposed based on the hybrid artificial intelligence algorithm. The proposed system explores the internal correlation between the color image and demand of users. In the proposed system, an artificial neural network based on the radial basis function is employed. The network model is trained with an improved particle swarm optimization combined with the weight-adaptive strategy and chaos theory. The proposed model predicts the multi-uses’ color images. Then, the decision colors are extracted from the predicted colors by K-harmonic means clustering. The experimental results show that the proposed color decision support system is promising in designing the color scheme and providing theoretical guidance for the product-color design.
Keywords: Decision support system | Artificial intelligence | Product design | Multi-users’ images
Models for estimating daily photosynthetically active radiation in oceanic and mediterranean climates and their improvement by site adaptation techniques
مدل های تخمین روزانه اشعه فتوسنتزی فعال در آب و هوای اقیانوسی و مدیترانه و بهبود آنها توسط تکنیک های سازگاری سایت-2020
In this work Photosynthetically Active Radiation (PAR) in oceanic and mediterranean climates is modeled. Twenty-two different models have been developed and tested: eleven Multilinear Regression (MR) models and eleven Artificial Neuron Network (ANN) models, using combinations of variables such as Global Horizontal Irradiance (GHI), Global Extraterrestrial Irradiance (G0), Temperature (T) and Relative Humidity (RH). Data provided by Satellite Application Facility on Climate Monitoring (CM SAF) are used to develop and train the models, while the models have been validated using field data from four stations located in Spain, covering the different study climates. According to the results, zones with different climate conditions need different models, both for the case of MR and ANN. The results show the need of including the GHI in all models in order to obtain accurate estimates; in fact, the presence of more variables only improves slightly the results in mediterranean climate, while in oceanic climate no improvement is observed. On the other hand, comparing MR and ANN models, ANN models did not show better results than those of MR models in no one of the cases studied. Regarding the climate, both types of models are clearly better for the mediterranean case than for the oceanic one. In order to improve the performance of the model for oceanic climate a correction based on the site adaptation technique was carried out. The good results obtained by this technique fully justify its use. The best proposed models provide better performance than other models which are restricted to certain locations. Besides, the clustering technique based on the PAR variable, used in this work, allows obtaining useful models for a whole region. Finally, another advantage of this methodology is that there is no need of ground measurements for its development, except for the site adaptation technique
Keywords: Photosynthetically active radiation | Site adaptation technique | Global horizontal irradiance | Artificial neuron network | Multilinear regression