scholarly journals Identifikasi Kesegaran Ikan Nila menggunakan Teknik Citra Digital

2020 ◽  
Vol 2 (1) ◽  
pp. 6-10
Author(s):  
Juli Elprida Hutagalung ◽  
Mhd Ihsan Pohan ◽  
Yuli Happy Marpaung
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Image Data ◽  
The Body ◽  
Training Data ◽  
Application System ◽  
Fresh Fish ◽  
Average Value ◽  
Color Changes ◽  
Image Format

Fish contain many nutrients that are very beneficial for the body, but often fish are traded in a state of death as well as being alive. To observe the freshness of tilapia is done by the introduction of color changes that appear on digital images using the least squares method. The purpose of this research is to build an image management application system to detect the freshness of tilapia. The data used are 10 samples of tilapia images which are photographed every 1 hour for 15 hours and obtained 150 image data and then processed and analyzed using the least squares method. The first process begins with image processing by cropping at the edge of the eye of the original image and then proceed with resizing to 1000 x 1000 pixels and changing the image format to *. Png. After the image has been processed then the average value is calculated rata grayscale uses the 'rata_rata Gambar' application system and an equation is stored which is stored as training data on the application system. After the image has been processed then the image is input into the system, the image will be converted into grayscale form and displayed at a predetermined place together with the rgb and grayscale histograms and then calculated using the least squares method. The last process we do is matching the test image with the image stored as training data and we conclude whether the image is (very fresh, fresh, fresh enough, not fresh, or very not fresh), the percentage of freshness of the anchor fish, and the length of time the anchor fish dies. This study used 150 samples of fish images from fresh fish that were still very fresh until the fish were not very fresh (rotten).

scholarly journals The Body Mass Index BMI Should be Abandoned in Favour of the Body-Shape Index BSI for Controlling Body Weight in Adults

2016 ◽  
Vol 1 (2) ◽  
Keyword(s):  
Weight Control ◽  
Body Shape ◽  
Least Squares Method ◽  
Shape Index ◽  
Normal Weight ◽  
Overweight And Obesity ◽  
The Body ◽  
Average Value ◽  
Males And Females ◽  
Standard Deviations

Objective: This paper aims at optimal metrology for defining healthy weights in humans using weight-height ratios. Study Design: Normal appearing Caucasian males and females of any age and height were stochastically selected individually and grouped into cohorts of gender, different heights and ages, in order to apply rigorous statistical analyses, using the least squares method of Gauss. Methods: 246 Caucasian males and 258 Caucasian females of “normal” appearance represent an unbiased stochastically selected cohort sufficiently large to analyse statistically individual and cohort values for Body-MassIndex, kg/m2 , and Body-Shape-Index, kg/m3 , relating to gender, height, and age. Results: For Caucasians taller than ~1.2m the BMI is largely inferior to the BSI. In adults, the single average normal weight BSI value is 12.54 for males and 12.36 for females, with standard deviations of 1.67 and 1.95, respectively. For children smaller than ~1.2m the BMI is superior showing at normal weight an average value of ~16.0 for males and ~15.2 for females, with standard deviations of 1.70 for males and 1.66 for females. The difference between BMI and BSI applicability lies in the proportionality of body shapes changing with growth from childhood to adults. Conclusions: The BMI is the choice for weight control only of children of <1.2m. In individuals taller than 1.7m, a single BMI value introduces serious errors and should not be used. The BSI provides a stable value with height >1.2m and should replace the BMI. - BSI and BMI cut-off values are given for severe underweight, overweight and obesity for males and females for clinical guidance and use in public health.

To verify or not to verify: using partial least squares to predict effect of online news on panic buying during pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kim-Lim Tan ◽  
Joseph Kee-Ming Sia ◽  
Daniel Kuok Ho Tang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Least Squares Method ◽  
Online News ◽  
The Body ◽  
Equation Modeling ◽  
Initial Attempt ◽  
Content Type ◽  
Partial Least Squares Method

PurposeCoronavirus disease (COVID-19) pandemic has given rise to different dimensions of uncommon human behavior, and panic buying is one of them. Interestingly, panic buying research has not been given much attention. The purpose of this paper is threefold. Firstly, it examines the influences of the theory of planned behavior (TPB) elements (subjective norm, attitude and perceived behavior control (PBC)) on panic buying. Secondly, it investigates online news and the perceived likelihood of being affected (PLA) as antecedents to the TPB constructs. Finally, to examine online news verification as a moderator on the relationship between the TPB constructs and panic buying.Design/methodology/approachData were collected from 371 respondents and analyzed using the partial least squares method structural equation modeling (PLS-SEM). PLS predict was applied to determine the predictive power of the model further.FindingsThis study found that subjective norms and attitude influence panic buying. The results further revealed that online news has a direct influence on the PLA and attitude. However, PBC has no such effect on panic buying. Surprisingly, online news verification also has no moderating effects on the relationships between the TPB elements and panic buying.Originality/valueThis research helps to understand consumer panic buying behavior, especially during shock events such as the COVID-19 pandemic. This study is the first that extends the TPB incorporating both online news and PLA as antecedents to panic buying in the same model. Furthermore, the study serves as an initial attempt to investigate online news verification as a moderator between the link of three constructs of TPB and panic buying, contributing to existing literature. Lastly, it advances the body of knowledge on consumer behavior and contributes methodologically by introducing the PLS approach.

An Application of Least Squares Method to the Solution of the Inverse Problem of Heat Conduction

1963 ◽  
Vol 85 (4) ◽  
pp. 378-379 ◽  
Author(s):  
Irving Frank
Keyword(s):  
Experimental Data ◽  
Inverse Problem ◽  
Heat Conduction ◽  
Least Squares ◽  
Heat Input ◽  
Least Squares Method ◽  
Least Square Method ◽  
The Body ◽  
Least Square

When the temperature of a body at some point is known, it is generally possible to determine the rate of heat input to the surface of the body. However, when the temperatures are determined experimentally, it will be found that there is some uncertainty in the solution for the rate of heat input. It is suggested that a least square method be used to determine the rate of heat input which best fits the experimental data.

scholarly journals A Case Study of Transfer of Lesion-Knowledge

2020 ◽  
Author(s):  
Soundarya Krishnan ◽  
Rishab Khincha ◽  
Lovekesh Vig ◽  
Tirtharaj Dash ◽  
Ashwin Srinivasan
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Brain Lesion ◽  
Image Data ◽  
Lung Lesion ◽  
The Body ◽  
Training Data ◽  
Model Transfer ◽  
The Brain

All organs in the human body are susceptible to cancer, and we now have a growing store of images of lesions in different parts of the body. This, along with the acknowledged ability of neural-network methods to analyse image data, would suggest that accurate models for lesions can now be constructed by a deep neural network. However an important difficulty arises from the lack of annotated images from various parts of the body. Our proposed approach to address the issue of scarce training data for a target organ is to apply a form of transfer learning: that is, to adapt a model constructed for one organ to another for which there are minimal or no annotations. After consultation with medical specialists, we note that there are several discriminating visual features between malignant and benign lesions that occur consistently across organs. Therefore, in principle, these features boost the case for transfer learning on lesion images across organs. However, this has never been previously investigated. In this paper, we investigate whether lesion knowledge can be transferred across organs. Specifically, as a case study,we examine the transfer of a lesion model from the brain to lungs and lungs to the brain. We evaluate the efficacy of transfer of a brain-lesion model to the lung, and the transfer of a lung-lesion model to the brain by comparing against a model constructed: (a) without model-transfer(i.e.random weights); and (b) using model-transfer from a lesion-agnostic dataset (ImageNet). In all cases, our lesion models perform substantially better. These results point to the potential utility of transferring lesion-knowledge across organs other than those considered here.

scholarly journals Solar vector solving method based on photovoltaic array and its application in attitude estimation of navigation system

2019 ◽  
Vol 53 (1-2) ◽  
pp. 151-163
Author(s):  
Hongmei Zhang ◽  
Huaqing Zhang ◽  
Guangyan Xu ◽  
Hao Liu
Keyword(s):  
Least Squares ◽  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Least Squares Method ◽  
The Body ◽  
Integrated Navigation ◽  
Measuring Device ◽  
Photovoltaic Array ◽  
Aerial Vehicles ◽  
Reflected Light

Solar-powered unmanned aerial vehicles usually fly at high altitudes, and they are mainly powered by the photocells covering the body of unmanned aerial vehicles. Considering that the solar vector cannot be affected by the disturbing magnetic field and harmful acceleration, a unit solar vector solving method based on photovoltaic array is proposed in this paper. The photocells with different installation angles are selected to form the photovoltaic array. The solar vector is solved by the least-squares method on the basis of normalization by using the output currents of the photovoltaic array. For eliminating the influence of faults and reflected light on the solving of the solar vector, an adaptive least-squares unit solar vector solving method is proposed. In addition, a solar vector measuring device is designed in order to verify the effectiveness of the proposed methods. By employing the structural advantages of the device, the current generated by the reflected light of the sky can be solved according to the currents generated by all photocells of this device. Thus, the solved current generated by the reflected light of the sky is more accurate. Moreover, strapdown inertial navigation system/solar vector/global positioning system integrated navigation Kalman filtering algorithm is proposed, in which the adaptive least-squares unit solar vector solving method is applied to the measurement update of the filter. The effectiveness of the methods proposed in this paper is illustrated by some numerical and physical simulations.

scholarly journals Uncorrected Least-Squares Temporal Difference with Lambda-Return

2020 ◽  
Vol 34 (04) ◽  
pp. 5323-5330
Author(s):  
Takayuki Osogami
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Linear Regression ◽  
Least Squares ◽  
Numerical Experiments ◽  
Least Squares Method ◽  
Training Data ◽  
Temporal Difference

Temporal difference, TD(λ), learning is a foundation of reinforcement learning and also of interest in its own right for the tasks of prediction. Recently, true online TD(λ) has been shown to closely approximate the “forward view” at every step, while conventional TD(λ) does this only at the end of an episode. We re-examine least-squares temporal difference, LSTD(λ), which has been derived from conventional TD(λ). We design Uncorrected LSTD(λ) in such a way that, when λ = 1, Uncorrected LSTD(1) is equivalent to the least-squares method for the linear regression of Monte Carlo (MC) return at every step, while conventional LSTD(1) has this equivalence only at the end of an episode, since the MC return is corrected to be unbiased. We prove that Uncorrected LSTD(λ) can have smaller variance than conventional LSTD(λ), and this allows Uncorrected LSTD(λ) to sometimes outperform conventional LSTD(λ) in practice. When λ = 0, however, Uncorrected LSTD(0) is not equivalent to LSTD. We thus also propose Mixed LSTD(λ), which % mixes the two LSTD(λ)s in a way that it matches conventional LSTD(λ) at λ = 0 and Uncorrected LSTD(λ) at λ = 1. In numerical experiments, we study how the three LSTD(λ)s behave under limited training data.

A LEAST‐SQUARES METHOD OF INTERPRETING MAGNETIC ANOMALIES CAUSED BY TWO‐DIMENSIONAL STRUCTURES

Geophysics ◽  
1969 ◽  
Vol 34 (1) ◽  
pp. 65-74 ◽  
Author(s):  
William W. Johnson
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Nonlinear Least Squares ◽  
Magnetic Anomalies ◽  
Descent Method ◽  
The Body ◽  
Steepest Descent Method ◽  
Two Dimensional ◽  
Gauss Method ◽  
Least Squares Estimates

The equations relating the magnetic anomalies to the shape and susceptibility of a body are nonlinear with respect to the coordinates describing the shape. Therefore, iterative procedures must be used to obtain least‐squares estimates of the body coordinates. One method in general use for obtaining nonlinear least‐squares estimates is the Gauss method. This method often fails when the initial values for the structures and susceptibilities do not adequately account for the magnetic anomalies. Another method known as the steepest descent method generally converges to a solution; however, a large number of iterations are required. A method suggested by Marquardt (1963) incorporates the best features of the previous methods. In this paper the Marquardt method is applied to the interpretation of magnetic anomalies. For this purpose the two‐dimensional formulas derived by Talwani and Heirtzler (1964) are used to relate the geometry of a body to the resulting magnetic anomalies. The procedure efficiently controls the amount of change made to an interpreted structure at each iteration, assuring rapid convergence to a solution which satisfies the observed data better in the least‐squares sense than does the initial solution. The method is applied to representative problems.

scholarly journals Application System for Identification of Surakarta Traditional Batik Images (SABATARA)

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 5
Author(s):  
Jani Kusanti ◽  
Ramadhian Agus T.S
Keyword(s):  
Image Data ◽  
Training Data ◽  
Test Results ◽  
Ministry Of Education ◽  
Test Image ◽  
Accuracy Rate ◽  
Application System ◽  
Occurrence Matrix ◽  
Contrast Stretching

Surakarta Batik is a traditional cloth in Indonesia that has been designated as an intangible cultural heritage by the Ministry of Education and Culture. The Surakarta Batik Pattern has characteristics and has a story in each style. The method used affects the accuracy of each pattern in the Surakarta batik image. Image data used for training data are 100 image data with a size of 256 x 256 pixels, with test image data used as many as 20 image data. Improving the quality of the image using contrast stretching, the output is processed to separate objects with the background using adaptive thresholding. The obtained object is added by the canny process and calculated using the Gray Level Co-Occurrence Matrix to obtain the characteristics of each image. The characteristics used are four variables (energy, contrast, homogeneity, and correlation). The resulting variable is used as input to the classification using backpropagation. The test results obtained an accuracy rate of 95%, with an error rate of 0.05%.

‘Least squares’ method—theorem or law?

1980 ◽  
Vol 59 (9) ◽  
pp. 8
Author(s):  
D.E. Turnbull
Keyword(s):  
Least Squares ◽  
Least Squares Method
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