scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

scholarly journals Forecasting peak energy demand for smart buildings

2020 ◽  
Author(s):  
Mona A. Alduailij ◽  
Ioan Petri ◽  
Omer Rana ◽  
Mai A. Alduailij ◽  
Abdulrahman S. Aldawood
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Energy Consumption ◽  
Energy Demand ◽  
Arima Model ◽  
Demand Forecasting ◽  
User Preferences ◽  
Machine Learning Techniques ◽  
Peak Energy ◽  
The Impact

AbstractPredicting energy consumption in buildings plays an important part in the process of digital transformation of the built environment, and for understanding the potential for energy savings. This also contributes to reducing the impact of climate change, where buildings need to increase their adaptability and resilience while reducing energy consumption and maintain user comfort. The use of Internet of Things devices for monitoring and control of energy consumption in buildings can take into account user preferences, event monitoring and building optimization. Detecting peak energy demand from historical building data can enable users to manage their energy use more efficiently, while also enabling real-time response strategies (including control and actuation) to known or future scenarios. Several statistical, time series, and machine learning techniques are proposed in this work to predict electricity consumption for five different building types, by using peak demand forecasting to achieve energy efficiency. We have used several indigenous and exogenous variables with a view to test different energy forecasting scenarios. The suggested techniques are evaluated for creating predictive models, including linear Regression, dynamic regression, ARIMA time series, exponential smoothing time series, artificial neural network, and deep neural network. We conduct the analysis on an energy consumption dataset of five buildings from 2014 until 2019. Our results show that for a day ahead prediction, the ARIMA model outperforms the other approaches with an accuracy of 98.91% when executed over a 168 h (1 week) of uninterrupted data for five government buildings.

scholarly journals Pose-Guided Part-Based Adaptive Pyramid Features for Occluded Person Reidentification

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiaobing Lin ◽  
Jilin Li ◽  
Zengxi Huang ◽  
Xiaoqin Tang
Keyword(s):  
Neural Network ◽  
Body Part ◽  
Experimental Results ◽  
Weighted Sum ◽  
Sum Rule ◽  
Partition Scheme ◽  
Variation Problem ◽  
Alignment Problem ◽  
Fully Connected ◽  
The Impact

Reidentifying an occluded person across nonoverlapping cameras is still a challenging task. In this work, we propose a novel pose-guided part-based adaptive pyramid neural network for occluded person reidentification. Firstly, to alleviate the impact of occlusion, we utilize pose landmarks to generate pose-guided attention maps. The attention maps will help the model focus on the nonoccluded regions. Secondly, we use pyramid pooling to extract multiscale features in order to address the scale variation problem. The generated pyramid features are then multiplied by attention maps to achieve pose-guided adaptive pyramid features. Thirdly, we propose a pose-guided body part partition scheme to deal with the alignment problem. Accordingly, the adaptive pyramid features are divided into partitions and fed into individual fully connected layers. In the end, all the part-based matching scores are fused with a weighted sum rule for person reidentification. The effectiveness of our method is clearly validated by the experimental results on two popular occluded and holistic datasets, i.e., Occluded-DukeMTMC and the Market-1501.

scholarly journals Impact of Symmetric Vertical Sinusoid Alignments on Infrastructure Construction Costs: Optimizing Energy Consumption in Metropolitan Railway Lines Using Artificial Neural Networks

2020 ◽  
Vol 6 (3) ◽  
pp. 145-156 ◽  
Author(s):  
J Pineda-Jaramillo ◽  
P. Salvador-Zuriaga ◽  
P. Martínez-Fernández ◽  
R. Insa-Franco
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Top Down ◽  
Construction Costs ◽  
Railway Line ◽  
Reduce Energy Consumption ◽  
Artificial Neural ◽  
Economic Perspectives ◽  
Artificial Neural Network Ann ◽  
The Impact

Abstract Minimizing energy consumption is a key issue from both an environmental and economic perspectives for railways systems; however, it is also important to reduce infrastructure construction costs. In the present work, an artificial neural network (ANN) was trained to estimate the energy consumption of a metropolitan railway line. This ANN was used to test hypothetical vertical alignments scenarios, proving that symmetric vertical sinusoid alignments (SVSA) can reduce energy consumption by up to 18.4% compared with a flat alignment. Finally, we analyzed the impact of SVSA application on infrastructure construction costs, considering different scenarios based on top–down excavation methods. When balancing reduction in energy consumption against infrastructure construction costs between SVSA and flat alignment, the extra construction costs due to SVSA have a return period of 25–300 years compared with a flat alignment, depending on the soil type and construction method used. Symmetric vertical sinusoid alignment layouts are thus suitable for scattered or soft soils, up to compacted intermediate geomaterials.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Impact of roof shading on building energy performance in warm & humid climatic places of India

2020 ◽  
pp. 50-64
Author(s):  
Kuladeep Kumar Sadevi ◽  
Avlokita Agrawal
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Energy Performance ◽  
General Assumption ◽  
Climatic Conditions ◽  
Building Envelope ◽  
Passive Cooling ◽  
Base Case ◽  
U Value ◽  
The Impact

With the rise in awareness of energy efficient buildings and adoption of mandatory energy conservation codes across the globe, significant change is being observed in the way the buildings are designed. With the launch of Energy Conservation Building Code (ECBC) in India, climate responsive designs and passive cooling techniques are being explored increasingly in building designs. Of all the building envelope components, roof surface has been identified as the most significant with respect to the heat gain due to the incident solar radiation on buildings, especially in tropical climatic conditions. Since ECBC specifies stringent U-Values for roof assembly, use of insulating materials is becoming popular. Along with insulation, the shading of the roof is also observed to be an important strategy for improving thermal performance of the building, especially in Warm and humid climatic conditions. This study intends to assess the impact of roof shading on building’s energy performance in comparison to that of exposed roof with insulation. A typical office building with specific geometry and schedules has been identified as base case model for this study. This building is simulated using energy modelling software ‘Design Builder’ with base case parameters as prescribed in ECBC. Further, the same building has been simulated parametrically adjusting the amount of roof insulation and roof shading simultaneously. The overall energy consumption and the envelope performance of the top floor are extracted for analysis. The results indicate that the roof shading is an effective passive cooling strategy for both naturally ventilated and air conditioned buildings in Warm and humid climates of India. It is also observed that a fully shaded roof outperforms the insulated roof as per ECBC prescription. Provision of shading over roof reduces the annual energy consumption of building in case of both insulated and uninsulated roofs. However, the impact is higher for uninsulated roofs (U-Value of 3.933 W/m2K), being 4.18% as compared to 0.59% for insulated roofs (U-Value of 0.33 W/m2K).While the general assumption is that roof insulation helps in reducing the energy consumption in tropical buildings, it is observed to be the other way when insulation is provided with roof shading. It is due to restricted heat loss during night.

Secular stagnation in the modern world

2018 ◽  
pp. 125-141 ◽  
Author(s):  
S. M. Drobyshevsky ◽  
P. V. Trunin ◽  
A. V. Bozhechkova
Keyword(s):  
Public Debt ◽  
Capital Accumulation ◽  
Technological Development ◽  
Japanese Economy ◽  
Modern World ◽  
Key Factors ◽  
Destruction Process ◽  
Secular Stagnation ◽  
The Impact

The paper studies the factors of secular stagnation. Key factors of long-term slowdown in economic growth include the slowdown of technological development, aging population, human capital accumulation limits, high public debt, creative destruction process violation etc. The authors analyze key theoretical aspects of long-term stagnation and study the impact of these factors on Japanies economy. The authors conclude that most of the factors have significant influence on the Japanese economy for recent decades, but they cannot explain all dynamics. For Russia, on the contrary, we do not see any grounds for considering the decline in the economy since 2013 as an episode of secular stagnation.

ANALISIS DETERMINAN KUALITAS LINGKUNGAN PERIODE 1999-2013

2016 ◽  
Vol 21 (1) ◽  
pp. 9-20
Author(s):  
Ersalina Tang
Keyword(s):  
Foreign Direct Investment ◽  
Energy Consumption ◽  
Gross Domestic Product ◽  
Co2 Emissions ◽  
Direct Investment ◽  
Meat Consumption ◽  
Domestic Product ◽  
World Data ◽  
The World ◽  
The Impact

The purpose of this study is to analyze the impact of Foreign Direct Investment, Gross Domestic Product, Energy Consumption, Electric Consumption, and Meat Consumption on CO2 emissions of 41 countries in the world using panel data from 1999 to 2013. After analyzing 41 countries in the world data, furthermore 17 countries in Asia was analyzed with the same period. This study utilized quantitative approach with Ordinary Least Square (OLS) regression method. The results of 41 countries in the world data indicates that Foreign Direct Investment, Gross Domestic Product, Energy Consumption, and Meat Consumption significantlyaffect Environmental Qualities which measured by CO2 emissions. Whilst the results of 17 countries in Asia data implies that Foreign Direct Investment, Energy Consumption, and Electric Consumption significantlyaffect Environmental Qualities. However, Gross Domestic Product and Meat Consumption does not affect Environmental Qualities.

European Economies Stability Faced With Potential Outburst of Sovereign Debt Crisis. An Empirical Study Using Neural Network

2013 ◽  
Vol 12 (2) ◽  
pp. 3255-3260
Author(s):  
Stelian Stancu ◽  
Alexandra Maria Constantin
Keyword(s):  
Neural Network ◽  
Sovereign Debt ◽  
Debt Crisis ◽  
Banking System ◽  
Sovereign Debt Crisis ◽  
European Level ◽  
Continuous Growth ◽  
Economic Context ◽  
European Economies ◽  
The Impact

Instilment, on a European level, of a state incompatible with the state of stability on a macroeconomic level and in the financial-banking system lead to continuous growth of vulnerability of European economies, situated at the verge of an outburst of sovereign debt crises. In this context, the current papers main objective is to produce a study regarding the vulnerability of European economies faced with potential outburst of sovereign debt crisis, which implies quantitative analysis of the impact of sovereign debt on the sensitivity of the European Unions economies. The paper also entails the following specific objectives: completing an introduction in the current European economic context, conceptualization of the notion of “sovereign debt crisis, presenting the methodology and obtained empirical results, as well as exposition of the conclusions.

The Nexus between Financial Development, Economic Growth and Energy Consumption in India: A VECM Technique

2020 ◽  
pp. 602-612
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Financial Development ◽  
Consumer Price Index ◽  
Development Economic ◽  
Energy Prices ◽  
Vector Error Correction ◽  
Percent Increase ◽  
Growth Energy ◽  
The Impact

The demand for energy consumption requires efficient financial development in terms of bank credit. Therefore, this study examines the nexus between Financial Development, Economic Growth, Energy Prices and Energy Consumption in India, utilizing Vector Error Correction Model (VECM) technique to determine the nature of short and long term relationships from 2010 to 2019. The estimation of results indicates that a one percent increase in bank credits to private sector results in 0.10 percent increase in energy consumption and 0.28 percent increase in energy consumption responses to 1 percent increase in economic growth. It is also observed that the impact of energy price proxied by consumer price index is statistically significant with a negative sign indicating the consistency with the theory.

Implication of Medical Treatment for Surgical Strategies in IBD

2019 ◽  
Vol 20 (13) ◽  
pp. 1363-1368
Author(s):  
Krisztina B. Gecse ◽  
Christianne J. Buskens
Keyword(s):  
Nutritional Status ◽  
Medical Treatment ◽  
Significant Proportion ◽  
Timing Of Surgery ◽  
Multidisciplinary Management ◽  
Key Factors ◽  
Surgical Strategies ◽  
Immunosuppressive Medications ◽  
Medical Paradigm ◽  
The Impact

Despite changing medical paradigm, still a significant proportion of patients with IBD require surgery. The patient's general condition, including nutritional status and the use of immunosuppressive medications is of great importance with regard to surgical complications, as well as the choice of optimal surgical strategy. The indication and the timing of surgery are key factors for the multidisciplinary management of IBD patients. The purpose of this review is to provide an overview on the impact of medical treatment on surgical strategies in IBD.

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