scholarly journals Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga

Petir ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 269-279
Author(s):  
Nurul Ramadhanti Hikmiyah ◽  
Riki Ruli A. Siregar ◽  
Budi Prayitno ◽  
Dine Tiara Kusuma ◽  
Novi Gusti Pahiyanti
Keyword(s):  
Real Time ◽  
Electricity Consumption ◽  
Subtractive Clustering ◽  
Average Value ◽  
Work From Home ◽  
Monitoring Process ◽  
Level Of Use ◽  
Medium Level ◽  
Online Schools ◽  
High Level

The use of electricity in household sector has increased, especially during the Covid-19 pandemic. The large number of activities carried out in home such as Work from Home, online schools, and online businesses caused difficulty to monitor the electricity consumption. The absence of electricity usage provisions affects the electricity monitoring process. Hence it takes a real time monitoring application of electricity consumption. Fuzzy subtractive clustering is an unsupervised method to form the number and center of clusters according to data conditions. This method serves to classify the household electricity users with the parameters used, is the amount of usage in rupiah and electric power. The grouping results from this method help users to monitoring electricity consumption in real time. The output describes the level of high, medium and low user electricity consumption. Based on the test results, the best Silhouette Coefficient value is 0.8322535 and three clusters are formed, with an accept ratio is 0.5, a reject ratio of 0.15, a radius of 1.7 and a squash factor of 0.5 hence a high level of use is obtained with an average value of the number of uses in IDR 655,993, power 2757 VA, medium level 240,553, 1071 VA and low level 46,479, 675 VA

scholarly journals Graph-Based Construction of 3D Korean Giwa House Models

Buildings ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 68
Author(s):  
Mankyu Sung
Keyword(s):  
Data Structure ◽  
Computer Graphics ◽  
Real Time ◽  
Texture Image ◽  
Time Rate ◽  
Commercial Software ◽  
Spline Curve ◽  
High Level ◽  
Complicated Part ◽  
Different Parts

This paper proposes a graph-based algorithm for constructing 3D Korean traditional houses automatically using a computer graphics technique. In particular, we target designing the most popular traditional house type, a giwa house, whose roof is covered with a set of Korean traditional roof tiles called giwa. In our approach, we divided the whole design processes into two different parts. At a high level, we propose a special data structure called ‘modeling graphs’. A modeling graph consists of a set of nodes and edges. A node represents a particular component of the house and an edge represents the connection between two components with all associated parameters, including an offset vector between components. Users can easily add/ delete nodes and make them connect by an edge through a few mouse clicks. Once a modeling graph is built, then it is interpreted and rendered on a component-by-component basis by traversing nodes in a procedural way. At a low level, we came up with all the required parameters for constructing the components. Among all the components, the most beautiful but complicated part is the gently curved roof structures. In order to represent the sophisticated roof style, we introduce a spline curve-based modeling technique that is able to create curvy silhouettes of three different roof styles. In this process, rather than just applying a simple texture image onto the roof, which is widely used in commercial software, we actually laid out 3D giwa tiles on the roof seamlessly, which generated more realistic looks. Through many experiments, we verified that the proposed algorithm can model and render the giwa house at a real time rate.

scholarly journals Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3956
Author(s):  
Youngsun Kong ◽  
Hugo F. Posada-Quintero ◽  
Ki H. Chon
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
Smartphone Application ◽  
Real Time Processing ◽  
Pain Stimulation ◽  
Significant Difference ◽  
Before And After ◽  
Electrical Pulses ◽  
Pain Detection ◽  
High Level

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

Urban network of China from the perspective of population mobility: Three-dimensional co-occurrence of nodes and links

2021 ◽  
pp. 0308518X2199781
Author(s):  
Xinyue Luo ◽  
Mingxing Chen
Keyword(s):  
Three Dimensional ◽  
Geographical Distance ◽  
Two Dimensional ◽  
Population Mobility ◽  
Urban Network ◽  
Urban Networks ◽  
Low Level ◽  
Medium Level ◽  
High Level ◽  
External Connections

The nodes and links in urban networks are usually presented in a two-dimensional(2D) view. The co-occurrence of nodes and links can also be realized from a three-dimensional(3D) perspective to make the characteristics of urban network more intuitively revealed. Our result shows that the external connections of high-level cities are mainly affected by the level of cities(nodes) and less affected by geographical distance, while medium-level cities are affected by the interaction of the level of cities(nodes) and geographical distance. The external connections of low-level cities are greatly restricted by geographical distance.

scholarly journals A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 627
Author(s):  
David Marquez-Viloria ◽  
Luis Castano-Londono ◽  
Neil Guerrero-Gonzalez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Interference Mitigation ◽  
Parallel Implementation ◽  
Computational Time ◽  
Successful Implementation ◽  
Interchannel Interference ◽  
The Difference ◽  
High Level ◽  
Performance Computing

A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 × 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol’s position in the constellation, achieving a reduction 47.25% reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

scholarly journals The Relationship between Symptom Flare of Atopic Dermatitis and Airborne Japanese Cedar and Cypress Pollen Counts: A Self-Scoring Diary Study

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Haruko Nishie ◽  
Mariko Kato ◽  
Shiori Kato ◽  
Hiroshi Odajima ◽  
Rumiko Shibata ◽  
...  
Keyword(s):  
Atopic Dermatitis ◽  
Real Time ◽  
Pollen Season ◽  
Airborne Pollen ◽  
Japanese Cedar ◽  
Diary Study ◽  
Pollen Counts ◽  
High Level ◽  
The Relationship

Background. With an increase in Japanese cedar and cypress (JC) pollinosis, the relationship between JC pollen and atopic dermatitis (AD) has been studied. Some reports suggest that JC pollen can be one exacerbating factor for AD, but there has been no report that discusses JC pollen counts relating to AD symptom flare although actual airborne JC pollen counts can widely fluctuate throughout the pollen season. Objective. The relationship between symptom flare of AD and airborne JC pollen counts was examined. Methods. We monitored JC pollen counts in real time and divided the counts into low and high level. We then analyzed self-scored “itch intensity” recorded by 14 AD patients through a self-scoring diary. Results. Among the 14 patients, 7 had significantly higher itch intensity while the pollen counts were high. Conclusion. Even during the pollen season, actual airborne pollen counts can widely fluctuate. Our study suggested that symptom flare of AD could be influenced by the actual pollen counts.

scholarly journals Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Data ◽  
Video Analytics ◽  
Web Based ◽  
Real Time Analysis ◽  
Open Source Framework ◽  
Cluster Configuration ◽  
Time Requirements ◽  
High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

scholarly journals Quality of colostral passive immunity and pattern of serum protein fluctuation in newborn calves

2003 ◽  
Vol 60 (3) ◽  
pp. 453-456 ◽  
Author(s):  
Patricia Pauletti ◽  
Raul Machado Neto ◽  
Irineu Umberto Packer ◽  
Raul Dantas D'Arce ◽  
Rosana Bessi
Keyword(s):  
Total Protein ◽  
Statistical Design ◽  
Extended Period ◽  
Experimental Period ◽  
Passive Immunity ◽  
Serum Total Protein ◽  
Non Linear ◽  
Medium Level ◽  
Group 2 ◽  
High Level

Immunity acquired by newborn animals is known as passive immunity, and for ruminants, antibody acquisition depends on the ingestion and absorption of adequate amounts of immunoglobulins from colostrum. This study relates different initial levels of acquired passive protection and serum total protein (TP) and immunoglobulin G (IgG). Serum immunoglobulin concentration and total protein were evaluated for female Holstein calves in the first sixty days of life. Animals were separated into three groups according to their initial level of passive immunity: group 1- animals with a low level of passive immunity (below 20 mg mL-1); group 2- animals with a medium level (between 20 and 30 mg mL-1), and group 3- animals with a high level (above 30 mg mL-1). Serum total protein was determined through the biuret method and IgG was determined by radial immunodiffusion. Data were analyzed as a completely randomized, split-plot statistical design. Fluctuation of the variables along the experimental period was determined through non-linear regression by the DUD method (PROC NLIN - Non Linear SAS). Animals with low antibody acquisition started to produce antibodies earlier, reflecting a compensatory synthesis. On the other hand, animals having adequate levels exhibited an extended period of immunoglobulin catabolism and the beginning of the endogenous phase was delayed. Regardless initial levels, the fluctuations in IgG contents occurred around adequate physiological concentrations, ranging from 20 to 25 mg mL-1.

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