scholarly journals Spatial distribution modelling of subsurface bedrock using a developed automated intelligence deep learning procedure: A case study in Sweden

2021 ◽  
Author(s):  
Abbas Abbaszadeh Shahri ◽  
Chunling Shan ◽  
Emma Zäll ◽  
Stefan Larsson
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Distribution Modelling ◽  
Learning Procedure

scholarly journals The Influence of Increased Salvage Felling on Forwarding Distance and the Removal—A Case Study from Croatia

Forests ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Andreja Đuka ◽  
Zoran Bumber ◽  
Tomislav Poršinsky ◽  
Ivica Papa ◽  
Tibor Pentek
Keyword(s):  
Spatial Distribution ◽  
Management Plan ◽  
Management Unit ◽  
Negative Effects ◽  
Forest Operations ◽  
Spatial Reference ◽  
Infrastructure Network ◽  
Gis Environment ◽  
Research Period

During the seven-year research period, the average annual removal was by 3274 m3 higher than the average annual removal prescribed by the existing management plan (MP). The main reason lies in the high amount of salvage felling volume at 55,238 m3 (38.3%) in both the main and the intermediate felling due to oak dieback. The analysis of forest accessibility took into account the spatial distribution of cutblocks (with ongoing felling operations) and the volume of felled timber for two proposed factors: (1) the position of the cutblock and (2) the position of the removal. Cutblock position factor took into account the spatial position of the felling areas/sites, while removal position factor besides the spatial reference took into account the amount of felled timber (i.e., volume) both concerning forest infrastructure network and forest operations. The analysed relative forest openness by using geo-processing workflows in GIS environment showed four types of opening areas in the studied management unit (MU): single-opened, multiple-opened, unopened and opened areas outside of the management unit. Negative effects of the piece-volume law and low harvesting densities on forest operations are highlighted in this research due to high amount of salvage felling particularly in the intermediate felling by replacing timber volume that should have come from thinnings.

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

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