scholarly journals A Road Condition Classification Algorithm for a Tire Acceleration Sensor using an Artificial Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 404
Author(s):  
Hyeong-Jun Kim ◽  
Jun-Young Han ◽  
Suk Lee ◽  
Jae-Ryon Kwag ◽  
Min-Gu Kuk ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
New Technologies ◽  
Road Surface ◽  
Safe Driving ◽  
Surface Conditions ◽  
The Road ◽  
Acceleration Data ◽  
Road Condition ◽  
Artificial Neural

The automotive industry is experiencing a period of innovation, represented by the term CASE (connected, autonomous, shared, and electric). Among the innovative new technologies for automobiles, intelligent tire (iTire) collects road surface information through sensors installed inside a tire and informs the driver of the road conditions. iTire can promote safe driving. Various kinds of research on iTire is ongoing, and this paper proposes an algorithm to determine the road surface conditions while driving. Specifically, we have proposed a method for extracting the feature points of a frequency band, by converting acceleration data collected by sensors through fast Fourier transform (FFT) and determining road surface conditions via an artificial neural network. Lastly, the applicability of the algorithm was verified.

scholarly journals An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 113 ◽  
Author(s):  
Joao Ferreira ◽  
Gustavo Callou ◽  
Albert Josua ◽  
Dietmar Tutsch ◽  
Paulo Maciel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Data Center ◽  
Data Centers ◽  
New Technologies ◽  
Operational Costs ◽  
The Us ◽  
Artificial Neural

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.

scholarly journals Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton

2019 ◽  
Vol 11 (11) ◽  
pp. 3024 ◽  
Author(s):  
Muhammad Hadi Saputra ◽  
Han Soo Lee
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Cellular Automaton ◽  
Lulc Changes ◽  
The Road ◽  
Ca Model ◽  
Artificial Neural ◽  
North Sumatra

Land use and land cover (LULC) form a baseline thematic map for monitoring, resource management, and planning activities and facilitate the development of strategies to balance conservation, conflicting uses, and development pressures. In this study, changes in LULC in North Sumatra, Indonesia, are simulated and predicted using an artificial-neural-network-based cellular automaton (ANN-CA) model. Five criteria (altitude, slope, aspect, distance from the road, and soil type) are used as exploratory data in the learning process of the ANN-CA model to determine their impacts on LULC changes between 1990 and 2000; among the criteria, altitude and distance from the road have strong impacts. Comparison between the predicted and the real LULC maps for 2010 illustrates high agreement, with a Kappa index of 0.83 and a percentage of correctness of 87.28%. Then, the ANN-CA model is applied to predict LULC changes in 2050 and 2070. The LULC predictions for 2050 and 2070 demonstrate high increases in plantation area of more than 4%. Meanwhile, forest and crop area are projected to decrease by approximately 1.2% and 1.6%, respectively, by 2050. By 2070, forest and crop areas will decrease by 1.2% and 1.7%, respectively, indicating human influences on LULC changes from forest and cropland to plantations. This study illustrates that the simulation of LULC changes using the ANN-CA model can produce reliable predictions for future LULC.

scholarly journals The effect of climate change on cholera disease: The road ahead using artificial neural network

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0224813 ◽  
Author(s):  
Zahra Asadgol ◽  
Hamed Mohammadi ◽  
Majid Kermani ◽  
Alireza Badirzadeh ◽  
Mitra Gholami
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
The Road ◽  
Artificial Neural

Artificial Neural Network Model for Road Pavement Classification using Features of Tire-Pavement Noise and Road Surface Images

2021 ◽  
Vol 263 (1) ◽  
pp. 5101-5105
Author(s):  
Seo Il Chang ◽  
Bo Kyeong Kim ◽  
Jae Kwan Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sound Intensity ◽  
Image Features ◽  
Road Surface ◽  
Intensity Level ◽  
Acoustic Features ◽  
Road Pavement ◽  
Artificial Neural ◽  
Pavement Noise

Artificial neural network models were developed to classify road pavement types into the transverse-tined, the longitudinal-tined, NGCS(Next Generation Concrete Surface), Diamond Grinding, and Stone Mastic Asphalt by utilizing tire-pavement noise and road surface images. Tire-pavement noise data were collected by OBSI(On-Board Sound Intensity) method, and analyzed to obtain sound intensity level, sound pressure level, and sound quality indices. Road surface image data was analyzed through image feature extraction algorithms of Hough transformation and HOG(Histogram of gradient). The important features among the acoustic and image characteristics were selected by a random forest model. The acoustic features selected by the random forest algorithm are the overall sound intensity level of 400~5kHz 1/3-octave bands, the sound intensities (W/m2) of 800~2kHz 1/3-octave bands, loudness, fluctuation strength and tonality. The image features selected are the number of longitudinal lines extracted from Hough transform algorithm and HOG of the central cell. The two groups of the selected features were applied separately or together to an artificial neural network model to find classification performance. The classification accuracy rates of the models using acoustic features only, image features only and both acoustic and image features combined were 90.8%, 88.8%, and 97.3%, respectively.

Study on the Prediction Meth of Automobile Braking Distance Based on Artificial Neural Network

2015 ◽  
Vol 734 ◽  
pp. 515-521
Author(s):  
Pei Ye ◽  
Xiu Mei Zhang ◽  
Tao Jiang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Influence Factors ◽  
The Road ◽  
The Neural Network ◽  
Artificial Neural Network Method ◽  
Braking Distance ◽  
Brake Performance ◽  
Artificial Neural

The automobile braking distance is one of the important indexes to measure the brake performance, so the study of automobile braking distance is very important. Domestic and foreign scholars research on automobile brake performance and braking distance, and achieved fruitful results, but there are only little research on the braking distance predicting of the car. In the paper, the process of automobile braking, effect of the braking distance, the influence factors and the road adhesion coefficient are studied. In the paper, it also discussed the effective methods to calculate the braking distance. On these basses, the author puts forward the prediction model of automobile braking distance with artificial neural network method. In this model, the author takes the running state and parameters of the car as samples for the input and output. After training, the author gets the curing prediction model based on each layer of network weights and threshold of the neural network.

scholarly journals MODEL PERUBAHAN PENUTUPAN/PENGGUNAAN LAHAN UNTUK IDENTIFIKASI LAHAN KRITIS DI KABUPATEN BOGOR, KABUPATEN CIANJUR, DAN KABUPATEN SUKABUMI

2016 ◽  
Vol 18 (1) ◽  
pp. 21 ◽  
Author(s):  
Siti Hadjar Kubangun ◽  
Oteng Haridjaja ◽  
Komarsa Gandasasmita
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Spatial Models ◽  
Change Models ◽  
The Road ◽  
Sustainable Environment ◽  
Main Factors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<div class="WordSection1"><p class="abstrak">Pemanfaatan lahan yang melampaui kemampuan lahannya, dapat mengakibatkan degradasi lahan. Degradasi lahan jika dibiarkan akan menimbulkan lahan kritis. Dampak yang terjadi akibat lahan kritis mengakibatkan lahan mengalami penurunan kualitas sifat-sifat tanah, penurunan fungsi konservasi, fungsi produksi, hingga berpengaruh pada kehidupan sosial dan ekonomi masyarakat yang memanfaatkan lahan tersebut. Penelitian ini bertujuan untuk mengidentifikasi lahan kritis, berdasarkan pemodelan perubahan penutupan/penggunaan lahan dengan metode <em>Artificial Neural Network</em> (ANN). Hasil penelitian ini menunjukkan bahwa lahan-lahan yang tergolong kritis mencakup lahan berlereng dengan penutupan/penggunaan lahan yang telah terkonversi. Faktor utama penyebab konversi lahan adalah tingginya kebutuhan hidup terhadap pangan, sandang, dan papan, akibat meningkatnya kepadatan penduduk. Selain hal tersebut, kemiringan lereng, jarak dari jalan, dan jarak dari permukiman juga menjadi faktor penyebab perubahan lahan. Upaya pemanfaatan lahan sebaiknya didukung oleh peningkatan kualitas sumber daya manusia, yang tidak hanya berorientasi pada kebutuhan sosial dan ekonomi, namun juga berorientasi pada lingkungan yang berkelanjutan.</p><p class="katakunci"><strong>Kata kunci</strong>:  jaringan saraf tiruan, perubahan penutupan/penggunaan lahan, model spasial, lahan kritis.</p><p class="judulABS"><strong>ABSTRACT</strong></p><p class="Abstrakeng"><em>Over used of land can caused the degradation, it can be lead to the critical of the land. The impacts of this issue such as the decreasing of the soil characteristics quality, conservation function, production, affecting social and economic of the society which used the land.  This research aims to identify the critical land based on the land use cover change models with Artificial Neural Network (ANN) method. This research shows the critical lands including land with the slope which has been converted with the land use cover change models. The main factors caused land converse are the high of need of food, clothing, and shelter, cause of the increasing population density. Besides those factors, the shape of slope, distance from the road and settlements   are also the result of the land changing. The efforts in using the land should be supported by the increasing of the human resources, which are not only be oriented on the need of social and economic, but also on the sustainable environment.</em></p><p class="katakunci"><em><strong>Keywords</strong>: artificial neural network (ANN), land use cover change (LUCC), spatial models, critical land.</em></p></div><strong><br clear="all" /></strong>

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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