Phenetic Research Articles

Taxicab Metric of a Different Kind

Summary Imagine being tasked with measuring the distance between two points in a city, but you can only use a taxicab’s odometer which displays only whole-number values (the integer part) of its distance traveled so far—such is the display of modern digital measuring devices. This coarse measurement between two points might be called, tongue in cheek, a taxicab metric, but clearly of a different sort from the taxicab metric on a discrete grid that we encounter in combinatorics. We will explore how we might apply probability to our taxicab’s inherent randomness, and along with limit theorems, indeed measure arbitrary distances with any desired accuracy.

Multi-objective trajectory planning in the multiple strata drilling process:A bi-directional constrained co-evolutionary optimizer with Pareto front learning

Trajectory planning is essential to ensure the safety and efficiency of the drilling process within varying downhole geological conditions. However, numerous constraints and dispersed objectives make this task challenging, resulting in a broken constrained Pareto front (PF) in the objective space. To address this issue, we present a novel multi-objective optimization algorithm (MOEA), the Bi-directional Constrained Co-evolutionary Optimizer (BCCO), for reducing the friction torque of the drilling string system and maintaining the wellbore stability of the directional drilling process in multiple changeable strata. Inspired by dual-population co-evolutionary mechanics, BCCO is thoughtfully developed to enhance complex constraints handling. An ESOINN is introduced to guide the population in exploring the irregular PF and adaptively approximating to it. In addition, a flexible constraint dominance principle is devised to filter out non-inferior infeasible solutions that fully balance feasibility, diversity, and convergence by combining constraint violation, minimum vector angle, and angle-penalized distance. Moreover, we adopt a minimum Manhattan distance (MMD) decision-making method to select the final reference solution for trajectory tracking control. To verify the effectiveness of BCCO, we compared it with seven state-of-the-art constrained MOEAs, four latest published MOEAs used in drilling trajectory planning (DTP) applications, and three ablation MOEAs revised from original BCCO on real-world constrained test instances and four different types of designed standard benchmarks. Extensive testing confirmed adaptability to diverse constrained PFs. BCCO displayed strong convergence and diversity when applied to real-world engineering problems while maintaining feasibility. To evaluate BCCO’s DTP performance, we built a wellbore stability model on actual geological formations and empirically tested it with both composite natural curves and third-order Bézier curves in various drilling trajectory scenarios. We established a comprehensive cloud-based DTP test system with the on-site Programmable Logic Controller (PLC) and the workstation via Data Transfer Unit (DTU) communication. Actual DTP cases confirmed the feasibility of BCCO’s Pareto solutions, with MMD-selected trajectories ensuring a harmonized drilling process.

PARROT: Prediction of enzyme abundances using protein-constrained metabolic models.

Protein allocation determines the activity of cellular pathways and affects growth across all organisms. Therefore, different experimental and machine learning approaches have been developed to quantify and predict protein abundance and how they are allocated to different cellular functions, respectively. Yet, despite advances in protein quantification, it remains challenging to predict condition-specific allocation of enzymes in metabolic networks. Here, using protein-constrained metabolic models, we propose a family of constrained-based approaches, termed PARROT, to predict how much of each enzyme is used based on the principle of minimizing the difference between a reference and an alternative growth condition. To this end, PARROT variants model the minimization of enzyme reallocation using four different (combinations of) distance functions. We demonstrate that the PARROT variant that minimizes the Manhattan distance between the enzyme allocation of a reference and an alternative condition outperforms existing approaches based on the parsimonious distribution of fluxes or enzymes for both Escherichia coli and Saccharomyces cerevisiae. Further, we show that the combined minimization of flux and enzyme allocation adjustment leads to inconsistent predictions. Together, our findings indicate that minimization of protein allocation rather than flux redistribution is a governing principle determining steady-state pathway activity for microorganism grown in alternative growth conditions.

Connected coordinated motion planning with bounded stretch

We consider the problem of connected coordinated motion planning for a large collective of simple, identical robots: From a given start grid configuration of robots, we need to reach a desired target configuration via a sequence of parallel, collision-free robot motions, such that the set of robots induces a connected grid graph at all integer times. The objective is to minimize the makespan of the motion schedule, i.e., to reach the new configuration in a minimum amount of time. We show that this problem is NP-complete, even for deciding whether a makespan of 2 can be achieved, while it is possible to check in polynomial time whether a makespan of 1 can be achieved. On the algorithmic side, we establish simultaneous constant-factor approximation for two fundamental parameters, by achieving constant stretch for constant scale. Scaled shapes (which arise by increasing all dimensions of a given object by the same multiplicative factor) have been considered in previous seminal work on self-assembly, often with unbounded or logarithmic scale factors; we provide methods for a generalized scale factor, bounded by a constant. Moreover, our algorithm achieves a constant stretch factor: If mapping the start configuration to the target configuration requires a maximum Manhattan distance of d, then the total duration of our overall schedule is O(d)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(d)$$\\end{document}, which is optimal up to constant factors.

Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading

Diabetic Retinopathy (DR) is a major source of sightlessness and permanent visual damage. Manual Analysis of DR is a labor-intensive and costly task that requires skilled ophthalmologists to observe and evaluate DR utilizing digital fundus images. The images can be employed for analysis and disease screening. This laborious task can gain a great advantage in automated detection by exploiting Artificial Intelligence (AI) techniques. Content-Based Image Retrieval (CBIR) approaches are utilized to retrieve related images in massive databases and are helpful in many application regions and most healthcare systems. With this motivation, this article develops the new Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification (MRFODL-FIRC) approach for the grading of DR. The suggested MRFODL-FIRC model investigates the retinal fundus imaging effectively to retrieve the relevant images and identify class labels. To achieve this, the MRFODL-FIRC technique uses Median Filtering (MF) as a pre-processing step. The Capsule Network (CapsNet) model is used to produce feature vectors with the MRFO algorithm as a hyperparameter optimizer. For the image retrieval process, the Manhattan distance metric is used. Finally, the Variational Autoencoder (VAE) model is used for recognizing and classifying DR. The investigational assessment of the MRFODL-FIRC technique is accomplished on medical DR and the outputs highlighted the improved performance of the MRFODL-FIRC algorithm over the current approaches.

K-NN’S NEAREST NEIGHBORS METHOD FOR CLASSIFYING TEXT DOCUMENTS BY THEIR TOPICS

Context. Optimization of the method of nearest neighbors k-NN for the classification of text documents by their topics and experimentally solving the problem based on the method.
 Objective. The study aims to study the method of nearest neighbors k-NN for classifying text documents by their topics. The task of the study is to classify text documents by their topics based on a dataset for the optimal time and with high accuracy.
 Method. The k-nearest neighbors (k-NN) method is a metric algorithm for automatic object classification or regression. The k-NN algorithm stores all existing data and categorizes the new point based on the distance between the new point and all points in the training set. For this, a certain distance metric, such as Euclidean distance, is used. In the learning process, k-NN stores all the data from the training set, so it belongs to the “lazy” algorithms since learning takes place at the time of classification. The algorithm makes no assumptions about the distribution of data and it is nonparametric. The task of the k-NN algorithm is to assign a certain category to the test document x based on the categories k of the nearest neighbors from the training dataset. The similarity between the test document x and each of the closest neighbors is scored by the category to which the neighbor belongs. If several of k’s closest neighbors belong to the same category, then the similarity score of that category for the test document x is calculated as the sum of the category scores for each of these closest neighbors. After that, the categories are ranked by score, and the test document is assigned to the category with the highest score.
 Results. The k-NN method for classifying text documents has been successfully implemented. Experiments have been conducted with various methods that affect the efficiency of k-NN, such as the choice of algorithm and metrics. The results of the experiments showed that the use of certain methods can improve the accuracy of classification and the efficiency of the model.
 Conclusions. Displaying the results on different metrics and algorithms showed that choosing a particular algorithm and metric can have a significant impact on the accuracy of predictions. The application of the ball tree algorithm, as well as the use of different metrics, such as Manhattan or Euclidean distance, can lead to improved results. Using clustering before applying k-NN has been shown to have a positive effect on results and allows for better grouping of data and reduces the impact of noise or misclassified points, which leads to improved accuracy and class distribution.

Numerical Computation of Distributions in Finite-State Inhomogeneous Continuous Time Markov Chains, Based on Ergodicity Bounds and Piecewise Constant Approximation

In this paper it is shown, that if a possibly inhomogeneous Markov chain with continuous time and finite state space is weakly ergodic and all the entries of its intensity matrix are locally integrable, then, using available results from the perturbation theory, its time-dependent probability characteristics can be approximately obtained from another Markov chain, having piecewise constant intensities and the same state space. The approximation error (the taxicab distance between the state probability distributions) is provided. It is shown how the Cauchy operator and the state probability distribution for an arbitrary initial condition can be calculated. The findings are illustrated with the numerical examples.

Electrocardiogram morphological arrhythmia classification using fuzzy entropy-based feature selection and optimal classifier

Electrocardiogram (ECG) signal analysis has become significant in recent years as cardiac arrhythmia shares a major portion of all mortality worldwide. To detect these arrhythmias, computer-assisted algorithms play a pivotal role as beat-by-beat monitoring of holter ECG signals is required. In this paper, a morphological arrhythmia classification algorithm has been proposed to classify seven different ECG beats, namely Normal Beat (N), Left Bundle Branch Block Beat (L), Right Bundle Branch Block Beat (R), Atrial Premature Contraction Beat (A), Premature Ventricular Contraction Beat (V), Fusion of Normal and Ventricle Beat (F) and Pace Beat (P). A novel feature set of 25 attributes has been extracted from each ECG beat and ranked using the Fuzzy Entropy-based feature selection (FEBFS) technique. In addition, two distinct classifiers, support vector machine with radial basis function as the kernel (SVM-RBF) and weighted K-nearest neighbor (WKNN), are used to categorize ECG beats, and their performances are also evaluated after adjusting vital parameters. The performance of classifiers is compared for four different ECG beat segmentation approaches and further analyzed using three similarity measurement techniques and two fuzzy entropy methods while feature selection. The classifier results are also cross-validated using a 10-fold cross-validation scheme, and the MIT-BIH Arrhythmia Database has been used to validate the proposed work. After selecting 21 highly ranked features, WKNN achieves the best results with the nearest neighbor value K = 3 and cityblock distance metrics, with Average Sensitivity (Sen) = 94.89%, Positive Predictivity (Ppre) = 97.13%, Specificity (Spe) = 99.72%, F1 Score = 95.95%, and Overall Accuracy (Acc) = 99.15%. The novelty of this work relies on formulating a unique feature set, including proposed symbolic features, followed by the FEBFS technique making this algorithm efficient and reliable for morphological arrhythmia classification. The above results demonstrate that the proposed algorithm performs better than many existing state-of-the-art works.

Power-law spectrum-based objective function to train a generative adversarial network with transfer learning for the synthetic breast CT image

Objective. This paper proposes a new objective function to improve the quality of synthesized breast CT images generated by the GAN and compares the GAN performances on transfer learning datasets from different image domains. Approach. The proposed objective function, named beta loss function, is based on the fact that x-ray-based breast images follow the power-law spectrum. Accordingly, the exponent of the power-law spectrum (beta value) for breast CT images is approximately two. The beta loss function is defined in terms of L1 distance between the beta value of synthetic images and validation samples. To compare the GAN performances for transfer learning datasets from different image domains, ImageNet and anatomical noise images are used in the transfer learning dataset. We employ styleGAN2 as the backbone network and add the proposed beta loss function. The patient-derived breast CT dataset is used as the training and validation dataset; 7355 and 212 images are used for network training and validation, respectively. We use the beta value evaluation and Fréchet inception distance (FID) score for quantitative evaluation. Main results. For qualitative assessment, we attempt to replicate the images from the validation dataset using the trained GAN. Our results show that the proposed beta loss function achieves a more similar beta value to real images and a lower FID score. Moreover, we observe that the GAN pretrained with anatomical noise images achieves better equality than ImageNet for beta value evaluation and FID score. Finally, the beta loss function with anatomical noise as the transfer learning dataset achieves the lowest FID score. Significance. Overall, the GAN using the proposed beta loss function with anatomical noise images as the transfer learning dataset provides the lowest FID score among all tested cases. Hence, this work has implications for developing GAN-based breast image synthesis methods for medical imaging applications.

Fingerprint Identification for Attendance Using Euclidean Distance and Manhattan Distance

Attendance is an action to confirm that someone is present at the office, school, or event. The use of attendance in an agency or company is really important as it can improve the level of discipline and productivity. However, the traditional way of doing attendance is considered less effective, less secure, and more difficult to organize. Therefore, a modern attendance system that utilizes fingerprints can be the right solution, especially because every fingerprint is unique. In this research, we focus on designing a fingerprint identification system for attendance purposes by using two distance measure methods, namely Euclidean Distance and Manhattan Distance. The dataset used in the research contains 111 fingerprint images with 90 images for training the designed fingerprint identification system and the remaining 21 images for testing the system. Each fingerprint image has undergone image pre-processing stage before being used. We compare Euclidean Distance and Manhattan Distance based on their performances in identifying fingerprint. From the test results, the fingerprint identification accuracy obtained using Euclidean Distance is 76.19%, while the accuracy obtained using Manhattan Distance is 71.43%. In general, both algorithms succeed in providing the correct identification results. This proves that Euclidean Distance and Manhattan Distance can be utilized for fingerprint identification purposes.

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection.

Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection. It overcomes the inherent limitations of IoU-based NMS variants to provide a more stable, consistent predictor of bounding box clustering by using a normalized Manhattan Distance inspired proximity metric to represent bounding box clustering. Unlike Greedy and Soft NMS, it does not rely solely on classification confidence scores to select optimal bounding boxes, instead selecting the box which is closest to every other box within a given cluster and removing highly confluent neighboring boxes. Confluence is experimentally validated on the MS COCO and CrowdHuman benchmarks, improving Average Precision by 0.2--2.7% and 1--3.8% respectively and Average Recall by 1.3--9.3 and 2.4--7.3% when compared against Greedy and Soft-NMS variants. Quantitative results are supported by extensive qualitative analysis and threshold sensitivity analysis experiments support the conclusion that Confluence is more robust than NMS variants. Confluence represents a paradigm shift in bounding box processing, with potential to replace IoU in bounding box regression processes.

Vicinity Vision Transformer.

Vision transformers have shown great success on numerous computer vision tasks. However, their central component, softmax attention, prohibits vision transformers from scaling up to high-resolution images, due to both the computational complexity and memory footprint being quadratic. Linear attention was introduced in natural language processing (NLP) which reorders the self-attention mechanism to mitigate a similar issue, but directly applying existing linear attention to vision may not lead to satisfactory results. We investigate this problem and point out that existing linear attention methods ignore an inductive bias in vision tasks, i.e., 2D locality. In this paper, we propose Vicinity Attention, which is a type of linear attention that integrates 2D locality. Specifically, for each image patch, we adjust its attention weight based on its 2D Manhattan distance from its neighbouring patches. In this case, we achieve 2D locality in a linear complexity where the neighbouring image patches receive stronger attention than far away patches. In addition, we propose a novel Vicinity Attention Block that is comprised of Feature Reduction Attention (FRA) and Feature Preserving Connection (FPC) in order to address the computational bottleneck of linear attention approaches, including our Vicinity Attention, whose complexity grows quadratically with respect to the feature dimension. The Vicinity Attention Block computes attention in a compressed feature space with an extra skip connection to retrieve the original feature distribution. We experimentally validate that the block further reduces computation without degenerating the accuracy. Finally, to validate the proposed methods, we build a linear vision transformer backbone named Vicinity Vision Transformer (VVT). Targeting general vision tasks, we build VVT in a pyramid structure with progressively reduced sequence length. We perform extensive experiments on CIFAR-100, ImageNet-1k, and ADE20K datasets to validate the effectiveness of our method. Our method has a slower growth rate in terms of computational overhead than previous transformer-based and convolution-based networks when the input resolution increases. In particular, our approach achieves state-of-the-art image classification accuracy with 50% fewer parameters than previous approaches.

Parametrų įvertinimas L1 normos metodu stochastiniame tiesiniame su vėlinimu augimo modelyje

This paper considered deterministic and stochastic delayed linear growth laws for the modeling of the process of growth. For the estimation of parameters the L1 distance procedure was proposed. As an illustrative experience was used a real data set from repeated measurements on permanent sample plots of pine stands in Lithuania.

Prediction and classification of sEMG-based pinch force between different fingers

The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning. The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers. The proposed methodology has the potential to realize simultaneous force and movement control of prosthetic hand.

A virtual sample generation method based on manifold learning and a generative adversarial network for soft sensor models with limited data

BackgroundIn the modern industrial process, data-driven-based soft sensor technology plays a key role in intelligent measurement. However, due to the strong dependence of the prediction accuracy on the training data, it can be challenging to apply this technology to particular industrial processes for which obtaining enough training samples is difficult. MethodsTo solve the problem of insufficient training data for soft sensor models, this paper proposes the Potential of Heat-diffusion for Affinity-based Trajectory Embedding and generative adversarial network based virtual sample generation (PGAN-VSG) method, which combines Potential of Heat-diffusion for Affinity-based Trajectory Embedding (PHATE) based feature extraction, virtual feature generation (VFG) techniques, and PHATE based generative adversarial network (PGAN) to generate virtual samples. The methodology of the work consists of several phases. First, the PHATE is deployed to embed the original data into a low-dimensional space to obtain the original feature set, which preserves the local and global structure of the original data. Second, a number of candidate features are randomly generated in the neighborhood space of each original feature, and the features located in the sparse feature region are selected as virtual features. Furthermore, the PGAN with L1 distance loss is used to map the virtual features to the corresponding high-dimensional space to obtain virtual samples. To validate the performance of the proposed PGAN-VSG method, a multilayer perceptron (MLP) model was employed as a soft sensor model to showcase the effectiveness of the proposed approach. This was achieved through both a numerical example and an industrial case study involving the penicillin fermentation process. Significant findingsBy filling the sample scarcity region while ensuring the distribution of generated samples, the virtual sample generation (VSG) method proposed in this paper improves the prediction accuracy of the soft sensor model while also outperforming the classical VSG method.

An Application of K-Nearest-Neighbor Regression in Maize Yield Prediction

Predictive analytics utilizes historical data and knowledge to predict future outcomes and provides a method for evaluating the accuracy and reliability of these forecasts. Artificial Intelligence is a tool of predictive analytics. AI trains computers to learn human behaviors like learning, judgment, and decision-making while simulating intelligent human behavior using computers and has received a lot of attention in almost all areas of research. Machine learning is a branch of Artificial Intelligence that has been used to solve classification and regression problems. Machine learning advancements have aided in boosting agricultural gains. Yield prediction is one of the agricultural sectors that has embraced machine learning. K Nearest Neighbor (KNN) Regression is a regression algorithm used in machine learning for prediction tasks. KNN Regression is like KNN Classification, except that KNN Regression predicts a constant output value for a given input instead of predicting a class label. The basic idea behind KNN Regression is to find the K nearest neighbors to a given input data point based on a distance metric and then use the average (or weighted average) of the output values of these K neighbors as the predicted output for the input data point. The distance metric used in KNN Regression can vary depending on the data type being analyzed, but common distance metrics include Euclidean distance, Manhattan distance, and Minkowski distance. This paper presents the application of KNN regression in maize yield prediction in Uasin Gishu county, in north rift region of Kenya. Questionnaires were distributed to 900 randomly selected maize farmers across the thirty wards to obtain primary data. With a Train Test split ration of 80:20, KNN regression algorithm was able to predict maize yield and its prediction performance was evaluated using Root Mean Squared error-RMSE=0.4948, Mean Squared Error-MSE =0.2803, Mean Absolute Error-MAE = 0.4591 and Mean Absolute Percentage Error-MAPE = 36.17. According to the study findings, the algorithm was able to predict maize yield in the maize producing county.

Methodology for estimation of intrinsic dimensions and state variables of microstructures.

According to the manifold hypothesis, real data can be compressed to lie on a low-dimensional manifold. This paper explores the estimation of the dimensionality of this manifold with an interest in identifying independent degrees of freedom and possibly identifying state variables that would govern materials systems. The challenges identified that are specific to materials science are (i) accurate estimation of the number of dimensions of the data, (ii) coping with the intrinsic random and low-bit-depth nature of microstructure samples, and (iii) linking noncompressed domains such as processing to microstructure. Dimensionality estimates are made with the maximum-likelihood-estimation method with the Minkowski p-norms being used as a measure of the distance between microstructural images. It is found that, where dimensionality estimates are required to be accurate, it is necessary to use the Minkowski 1-norm (also known as the L_{1}-norm or Manhattan distance). This effect is found to be due to image quantification and proofs are given regarding the distortion produced by quantization. It is also found that homogenization is an effective way of estimating the dimension of random microstructure image sets. An estimate of 40 dimensions for the fibers of a SiC/SiC fiber composite is obtained. It is also found that, with images generated from a sparse domain (surrogate to the process domain), it is possible to infer the nature of the process manifold from images alone.

Improved OAKR approach to condition monitoring of rotating machinery

Faults in main subsystems or components of a rotating machine often causes unscheduled shutdown, which may lead to not only huge economic losses, but also safety accidents. As an important part of intelligent maintenance, condition monitoring becomes a powerful tool in reducing maintenance costs through automatic fault alarming, thereby reducing potential downtime while improving system safety and reliability. An optimized auto-associative kernelregression (OAKR) model has been proposed recently and demonstrated as a promising tool for condition monitoring of various turbomachines, which is independent of fault mode and machine type. However, the fault identification accuracy of this approach largely relies on data quality in practical applications. Data incompleteness, parameter variation and system complexity often result in the inaccuracy of fault alarming for complicated rotating machinery. This paper proposes an improved OAKR method to address these issues, including utilizing wavelet packet Bayesian thresholding method (WPB) to reduce noise in the raw multivariate data, developing the Manhattan distance to calculate the sample similarity, and constructing a multivariate health index based on Multivariate Permutation Entropy to identify potential faults in equipment condition monitoring. Parametric analysis and a comparison study with original AAKR and OAKR methods by using the actual data of a gas turbine are conducted to illustrate the effectiveness and feasibility of the proposed methodology.

Vigilance estimation using truncated l1 distance kernel-based sparse representation regression with physiological signals

BackgroundWith a large number of accidents caused by the decline in the vigilance of operators, finding effective automatic vigilance monitoring methods is a work of great significance in recent years. Based on physiological signals and machine learning algorithms, researchers have opened up a path for objective vigilance estimation. MethodsSparse representation (SR)-based recognition algorithms with excellent performance and simple models are very promising approaches in this field. This paper aims to study the adaptability and performance improvement of truncated l1 distance (TL1) kernel on SR-based algorithm in the context of physiological signal vigilance estimation. Compared with the traditional radial basis function (RBF), the TL1 kernel has good adaptiveness to nonlinearity and is suitable for the discrimination of complex physiological signals. A recognition framework based on TL1 and SR theory is proposed. Firstly, the inseparable physiological features are mapped to the reproducing kernel Kreĭn space through the infinite-dimensional projection of the TL1 kernel. Then the obtained kernel matrix is converted into the symmetric positive definite matrix according to the eigenspectrum approaches. Finally, the final prediction result is obtained through the sparse representation regression process. ResultsWe verified the performance of the proposed framework on the popular SEED-VIG dataset containing physiological signals (electroencephalogram and electrooculogram) associated with vigilance. In the experimental results, the TL1 kernel is superior to the RBF kernel in both performance and kernel parameter stability. ConclusionsThis demonstrates the effectiveness of the TL1 kernel in distinguishing physiological signals and the excellent vigilance estimation capability of the proposed framework. Moreover, the contribution of our research motivates the development of physiological signal recognition based on kernel methods.

Efficiency design of mini factory for soy protein concentrate-based high protein biscuits

Choosing the right method for planning a mini-plant layout with the most efficient material handling is crucial to minimize its cost of production. This study aims to create the efficient layout design for soy concentrate-based high protein biscuit mini factory, as well as to evaluate the financial feasibility of the mini factory with the most efficient layout design. Layout generation methods used in this study were the Systematic Layout Planning and BLOCPLAN algorithm. The layout efficiency was evaluated through the calculation of rectilinear distance and material handling cost. The financial aspects on the feasibility study were evaluated through the calculation of rectilinear distance and material handling cost. The SLP method was able to generate the most efficient layout design with a material handling distance of 13.78 m and material handling cost of IDR 79,537 per month, while the BLOCPLAN algorithm was able to generate a layout design with a material handling distance of 15.56 m and material handling cost of IDR 89,397 per month. It could be concluded that the most efficient layout design for the high-protein biscuit mini factory was the one generated from the SLP method, which had the Net Present Value of IDR 14,619,251; Internal Rate of Return of 12.38%, and Payback Period of 4.42 years.