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.
The design of software development platform for CFETR plasma control system
طراحی بستر توسعه نرم افزار برای سیستم کنترل پلاسما CFETR-2020
The Plasma Control System (PCS) is a critical system of the tokamak device to guarantee the physical experiment operation. While the Chinese Fusion Engineering Testing Reactor (CFETR) PCS is in the preliminary development stage, the newly designed Plasma Control System Software Development Platform (PCS-SDP) will provide an effective, convenient, and visual development environment for PCS software developers. The PCS-SDP is developed based on the Eclipse framework as an extension and finally realized as an Eclipse plug-in. It is deployed in a thin-client C/S mode in which developers log in and work remotely and all the developments are carried on a development server. The PCS-SDP possesses an intuitive UI and contains modules of project management, algorithm management, visualization management, testing management, and version management. Because of these customized functions, the PCS-SDP makes the developers focus on the control logic design of the PCS algorithms without the need to pay attention to the PCS details; the work efficiency is improved significantly. In this paper, the requirements are analyzed, the system architecture and module design are presented, and some functions are demonstrated. The initial hardware environment deployment has been implemented and is also presented in this paper. Further efforts will be made to implement and demonstrate the functions of all modules on the EAST PCS, then serve CFETR PCS development and can be appropriate for most Plasma Control Systems
Keywords: Software platform | Plasma control system | Eclipse | Visualization | Algorithm management
3D pattern identification approach for cooling load profiles in different buildings
روش شناسایی الگوی سه بعدی برای خنک کردن پروفایل بار در ساختمانهای مختلف-2020
Building energy conservation has gained increasing concern owing to its large portion of energy consumption and great potential of energy saving. In-depth understanding of representative patterns of daily cooling load profile will facilitate effective building energy system scheduling, fault detection and diagnosis, as well as demand and supply side management. In this study, a novel three-stage approach is proposed for pattern identification of cooling load profiles in different types of buildings. The three stages include data preparation, data clustering and data visualization. The initial measurement in the building energy management system is conducted at the time step of 15 min. To further explore the characteristics of the building cooling load trend, 1-h mean pattern, 4-h mean pattern and daily statistical information (i.e. average, minimum and maximum values) of cooling load are also adopted for data clustering, respectively. To test the generality and robustness of the proposed approach, one-year historical measurement data collected from the practical chilled water system in two different buildings are adopted, respectively. The analysis demonstrates that the 3D pattern identification approach can effectively discover the representative characteristics of the daily cooling load profiles in both buildings. It is also expected that the proposed 3-stage pattern identification approach is general in adoption and can be potentially adopted in various types of buildings in different climate zones.
Keywords: Pattern identification | Gaussian mixture model clustering | Cooling load | Data visualization | Energy management
Genome Annotator Light (GAL): A Docker-based package for genome analysis and visualization
نور حاشیه نویسی ژنوم (GAL): یک بسته مبتنی بر داکر برای تجزیه و تحلیل ژنوم و تجسم ژنوم-2020
Next generation sequencing techniques produce enormous data but its analysis and visualization remains a big challenge. To address this, we have developed Genome Annotator Light(GAL), a Docker based package for genome analysis and data visualization. GAL integrated several existing tools and in-house programs inside a Docker Container for systematic analysis and visualization of genomes through web browser. GAL takes varieties of input types ranging from raw Fasta files to fully annotated files, processes them through a standard annotation pipeline and visualizes on a web browser. Comparative genomic analysis is performed automatically within a given taxonomic class. GAL creates interactive genome browser with clickable genomic feature tracks; local BLAST-able database; query page, on-fly downstream data analysis using EMBOSS etc. Overall, GAL is an extremely convenient, portable and platform independent. Fully integrated web-resources can be easily created and deployed, e.g. www.eumicrobedb.org/cglab, for our in-house genomes. GAL is freely available at https:// hub.docker.com/u/cglabiicb/.
Keywords: Genome analysis | Genome browser | Docker | EMBOSS | Visualization
Impact of a visual decision support tool in project control: A comparative study using eye tracking
تأثیر یک ابزار پشتیبانی از تصمیم بصری در کنترل پروژه: یک مطالعه مقایسه ای با استفاده از ردیابی چشم-2020
This paper presents the results of a comparative study where two decision support tools in project control have been selected: the S-Curve and Activity Gazer. The objective of the research is to characterize the impact of the visual decision-support tool in project control on the project planners decision. Using eye tracking, a withinsubject experiment was conducted with 17 participants where they were asked to make a diagnostic on a project portfolio. Results show that, despite the fact that a representation using the S-Curve helps reduce the time of diagnostics, both tools seem to have the same effect on the quality of the diagnostic by the participant. Also, we find that a representation where Activity Gazer is present is less mentally demanding than a representation where the S-Curve is present. These results suggest that the S-Curve could be improved to reduce the mental charge needed to analyze it and that new visualization tools could help project planners in their daily work.
Keywords: Project control | Visualization | Eye tracking | Activity Gazer | Project management | Decision support
Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities
هوش مصنوعی در صنعت AEC: تجزیه و تحلیل ساینومتریک و تجسم فعالیتهای تحقیقاتی-2020
The Architecture, Engineering and Construction (AEC) industry is fraught with complex and difficult problems. Artificial intelligence (AI) represents a powerful tool to assist in addressing these problems. Therefore, over the years, researchers have been conducting research on AI in the AEC industry (AI-in-the-AECI). In this paper, the first comprehensive scientometric study appraising the state-of-the-art of research on AI-in-the-AECI is presented. The science mapping method was used to systematically and quantitatively analyze 41,827 related bibliographic records retrieved from Scopus. The results indicated that genetic algorithms, neural networks, fuzzy logic, fuzzy sets, and machine learning have been the most widely used AI methods in AEC. Optimization, simulation, uncertainty, project management, and bridges have been the most commonly addressed topics/ issues using AI methods/concepts. The primary value and uniqueness of this study lies in it being the first in providing an up-to-date inclusive, big picture of the literature on AI-in-the-AECI. This study adds value to the AEC literature through visualizing and understanding trends and patterns, identifying main research interests, journals, institutions, and countries, and how these are linked within now-available studies on AI-in-the-AECI. The findings bring to light the deficiencies in the current research and provide paths for future research, where they indicated that future research opportunities lie in applying robotic automation and convolutional neural networks to AEC problems. For the world of practice, the study offers a readily-available point of reference for practitioners, policy makers, and research and development (R&D) bodies. This study therefore raises the level of awareness of AI and facilitates building the intellectual wealth of the AI area in the AEC industry.
Keywords: Architecture-engineering-construction | Artificial intelligence | Machine intelligence | Industry 4.0 | Automation | Digital transformation | Scientometric | Review
What are we discarding during the life cycle of a building? Case studies of social housing in Andalusia, Spain
در طول چرخه زندگی یک ساختمان چه چیزی را دور می زنیم؟ مطالعات موردی مسکن اجتماعی در اندلس ، اسپانیا-2020
The paper evaluates for the first time the embodied impact in CDW during the buildings life cycle by means of the bill of quantities of construction projects. The main objective is to be able to predict the future CDW to be generated by a project in the design stage, by means of the bill of quantities of the urbanization, construction, renovation, rehabilitation and demolition projects. The tools already in place for cost control can be used as an instrument for the introduction of sustainability considerations in construction projects. The methodology proposes a connection between the different stages of a building’s life cycle, more precisely its budget. The latter is linked to other future budgets for building renovations or retrofitting projects. The result shows that urbanization and demolition generate 90% of CDW, the former is caused by earthworks and the latter is due to the elimination of all building materials. The building is removed 1.3 times, in terms of material weight, energy and water. Finally, traditional models for economic control and waste management in construction projects can be the vector which introduce environmental assessment through the building life cycle.
Keywords: Embodied energy | Embodied water | Economic impact | Urbanization | Construction and demolition waste | Social housing
GeoVReality: A computational interactive virtual reality visualization framework and workflow for geophysical research
GeoVReality: چارچوب تجسم واقعیت مجازی تعاملی محاسباتی و گردش کار برای تحقیقات ژئوفیزیکی-2020
We present a new interactive computational virtual reality (VR) visualization framework for geophysical Big Data and models for the development of immersive collaborative virtual reality applications with a focus on targeted processing and interaction of Big Data. The framework includes a high-performance scalable persistent storage solution for the spatial analysis of Geospatial Information System (GIS), which uses an engine based on efficient in-memory computing. To more effectively visualize and interact in a VR environment, a machine learning algorithm library is used for compressing and extracting visual data. The framework supports mainstream rendering engines and VR hardware. The framework is extensible, customizable, cross-platform, and it is based only on open source tools. A workflow was introduced, and the geophysical data visualization and interaction effects were demonstrated by taking the abyss data of the Mariana Trench as example.
Keywords: Virtual reality | Geophysical model | Interactive visualization | Unreal engine | Unity 3D | Big data
Embodied carbon assessment of residential housing at urban scale
ارزیابی کربن تجسم یافته مسکن مسکونی در مقیاس شهری-2020
A great majority of the previous research put extensive efforts on the evaluation of life cycle impacts and carbon footprint of single buildings. Analysis on single buildings often excludes components related with urban scale such as construction of infrastructure, distance to city centre and transportation. Research on neighbourhoodscale settlements is necessary to further develop the understanding of the environmental impact of builtenvironment. This study aims to develop a Life Cycle Assessment (LCA) framework for the embodied carbon assessment of the built environment at neighbourhood scale. The study validates the results on three neighbourhood-scale mass housing projects in Ankara, Turkey. Embodied carbon assessment of these projects were conducted in order to generate a reference model of mass-housing projects. A data management framework for carbon assessment was also provided in the study. According to the results, an average of 409.2 kgCO2-eq/m2 originated at the neighbourhood level including emissions from the buildings, structural landscape and transportation infrastructure. Buildings contribute 272.4 kgCO2-eq/m2, which comprise 66.6% of the total emissions. On the other hand, 37.4 kgCO2-eq/m2 (9.1%) originate from structural landscape and 99.4 kgCO2-eq/m2 (24.3%) originate from transportation infrastructure. The results reveal the necessity to widen the assessment boundaries when investigating embedded carbon in larger scale built environment for fairer results. The outputs of the model may yield valuable inputs for designers and urban planners throughout their decision-making processes.
Keywords: Embodied carbon | Life cycle assessment (LCA) | Mass housing projects | Transportation | Neighbourhood-scale development | Data management
Visualizing Mitochondrial Form and Function within the Cell
تجسم فرم و عملکرد میتوکندری در سلول-2020
The specific cellular role of mitochondria is influenced by the surrounding environment because effective mitochondrial function requires the delivery of inputs (e.g., oxygen) and export of products (e.g., signaling molecules) to and from other cellular components, respectively. Recent technological developments in mitochondrial imaging have led to a more precise and comprehensive understanding of the spatial relationships governing the function of this complex organelle, opening a new era of mitochondrial research. Here, I highlight current imaging approaches for visualizing mitochondrial form and function within complex cellular environments. Increasing clarity of mitochondrial behavior within cells will continue to lend mechanistic insights into the role of mitochondria under normal and pathological conditions and point to spatially regulated processes that can be targeted to improve cellular function
A novel method for malware detection on ML-based visualization technique
یک روش جدید برای شناسایی بدافزارها در تکنیک تجسم مبتنی بر ML-2020
Malware detection is one of the challenging tasks in network security. With the flourishment of network techniques and mobile devices, the threat from malwares has been of an increasing significance, such as metamorphic malwares, zero-day attack, and code obfuscation, etc . Many machine learning (ML)-based malware detection methods are proposed to address this problem. However, considering the attacks from adversarial examples (AEs) and exponential increase in the malware variant thriving nowadays, malware detection is still an active field of research. To overcome the current limitation, we proposed a novel method using data visualization and adversarial training on ML-based detectors to efficiently detect the different types of malwares and their variants. Experimental results on the MS BIG malware database and the Ember database demonstrate that the proposed method is able to prevent the zero-day attack and achieve up to 97.73% accuracy, along with 96.25% in average for all the malwares tested.
Keywords: Malware detection | Adversarial training | Adversarial examples | Image texture | Data visualization