Variable Dimension Research Articles

Necessary Optimality Conditions for Hybrid System of Variable Dimension with Intermediate Constraints

Necessary Optimality Conditions for Hybrid System of Variable Dimension with Intermediate Constraints

The Leadership Style of the Head of the Health Center and Its Effect on the Quality of Patient Service

This study aims to determine the influence of the leadership style of the head of the public health center on the quality of patient care at the public health center, Baubau City. This study uses a quantitative research design, in which the data collected from each variable dimension in the form of numbers and then processed statistically so that it can produce conclusions that are statistically recognized. The population of this study were patients who had received a treatment at the Baubau City Health Center and officers who worked at the health center which were mapped in 17 health centers areas in Baubau City. Research data were analyzed using 2 types of analysis, namely univariate analysis to see the frequency distribution of each variable dimension, and bivariate analysis to see the relationship between the independent variable and the dependent variable. The results showed that the leadership style of the head of the public health center in Baubau City can foster morale in serving patients, with an average score of 3.1 or categorized as good. The quality of patient service at the Baubau City Health Center shows that it has been carried out according to the procedure, with an average score of 3.6 or categorized as good. The conclusion of this study shows that the leadership style of the head of the community health center has a positive and significant effect on the quality of patient care at the public health center in Baubau City by 91.8%.

Stress sensitivity of carbonate gas reservoirs and its microscopic mechanism

In order to evaluate the stress sensitivity of carbonate reservoirs, a series of rock stress sensitivity tests were carried out under in-situ formation temperature and stress condition. Based on the calibration of capillary pressure curve, the variable fractal dimension was introduced to establish the conversion formula between relaxation time and pore size. By using the nuclear magnetic resonance (NMR) method, the pore volume loss caused by stress sensitivity within different scales of pore throat was quantitatively analyzed, and the microscopic mechanism of stress sensitivity of carbonate gas reservoirs was clarified. The results show that fractures can significantly affect the stress sensitivity of carbonate reservoirs. With the increase of initial permeability, the stress sensitivity coefficient decreases and then increases for porous reservoirs, but increases monotonously for fractured-porous reservoirs. The pore volume loss caused by stress sensitivity mainly occurs for mesopores (0.02–0.50 μm), contributing more than 50% of the total volume loss. Single high-angle fracture contributes 9.6% of the stress sensitivity and 15.7% of the irreversible damage. The microscopic mechanism of the stress sensitivity of carbonate gas reservoirs can be concluded as fracture closure, elastic contraction of pores and plastic deformation of rock skeleton.

Heterogeneous Domain Adaptation With Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity.

Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution. This becomes more challenging when the source and target domains are in heterogeneous feature spaces, known as heterogeneous domain adaptation (HDA). While most HDA methods utilize mathematical optimization to map source and target data to a common space, they suffer from low transferability. Neural representations have proven to be more transferable; however, they are mainly designed for homogeneous environments. Drawing on the theory of domain adaptation, we propose a novel framework, Heterogeneous Adversarial Neural Domain Adaptation (HANDA), to effectively maximize the transferability in heterogeneous environments. HANDA conducts feature and distribution alignment in a unified neural network architecture and achieves domain invariance through adversarial kernel learning. Three experiments were conducted to evaluate the performance against the state-of-the-art HDA methods on major image and text e-commerce benchmarks. HANDA shows statistically significant improvement in predictive performance. The practical utility of HANDA was shown in real-world dark web online markets. HANDA is an important step towards successful domain adaptation in e-commerce applications.

Construction of variable dimension green high energy complex and laser response

A high dimensional energetic complex [Co(OH-Tz)2(H2O)2]n was synthesis with tetrazole energetic ligand, and its single crystal structures were obtained. Compared with the previously reported complexes, it has higher density (ρ = 2.135 g·cm−3), higher energy level (D = 7.59 km·s−1) and thermal stability (Tdec = 271 ℃). In addition, the femtosecond laser and high-speed imaging results show that this compound is sensitive to laser stimulation, and under the stimulation of 808 nm femtosecond laser, the detonation process initiated by thermal mechanism can occur when the total laser energy is 224 mJ. Therefore, it is a potential environment-friendly laser detonating material.

Advancing 3D medical image analysis with variable dimension transform based supervised 3D pre-training

The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications. Therefore, it is non-trivial to build well-performing 3D convolutional neural networks from scratch. Previous efforts on 3D pre-training have frequently relied on self-supervised approaches, which use either predictive or contrastive learning on unlabeled data to build invariant 3D representations. However, because of the unavailability of large-scale supervision information, obtaining semantically invariant and discriminative representations from these learning frameworks remains problematic. In this paper, we revisit an innovative yet simple fully-supervised 3D network pre-training framework to take advantage of semantic supervision from large-scale 2D natural image datasets. With a redesigned 3D network architecture, reformulated natural images are used to address the problem of data scarcity and develop powerful 3D representations. Comprehensive experiments on five benchmark datasets demonstrate that the proposed pre-trained models can effectively accelerate convergence while also improving accuracy for a variety of 3D medical imaging tasks such as classification, segmentation, and detection. In addition, as compared to training from scratch, it can save up to 60% of annotation efforts. On the NIH DeepLesion dataset, it also achieves state-of-the-art detection performance, outperforming earlier self-supervised and fully-supervised pre-training approaches, as well as methods that do training from scratch. To facilitate further development of 3D medical models, our code and pre-trained model weights are publicly available at https://github.com/urmagicsmine/CSPR.

Failure to Achieve Domain Invariance With Domain Generalization Algorithms: An Analysis in Medical Imaging

One prominent issue in the application of deep learning is the failure to generalize to data that lies on a different distribution to the training data. While many methods have been proposed to address this, prior work has shown that when operating under the same conditions most algorithms perform almost equally. As such, more work needs to be done to validate past and future methods before they are put into important scenarios like medical imaging. Our work analyses eight domain generalization algorithms across four important medical imaging classification datasets along with three standard natural image classification problems to discover the differences in how these methods operate in these different contexts. We assess these algorithms in terms of generalization capability, domain invariance, and representational sensitivity. Through this, we show that despite the differences between domain and content variations between natural and medical imaging there is little deviation in the operation of each method between natural images and medical images. Additionally, we show that all tested algorithms retain significant amounts of domain-specific information in their feature representations despite explicit training to remove it. Thus, revealing the failure point of all these methods is a lack of class-discriminative features extracted from out-of-distribution data. While these results show that methods that work well on natural imaging work similarly in medical imaging, no method outperforms baseline methods, highlighting the continuing gap of achieving adequate domain generalization. Similarly, the results also question the efficacy of optimizing for domain invariant representations as a method for generalizing to unseen domains.

Seeing Through Darkness: Visual Localization at Night via Weakly Supervised Learning of Domain Invariant Features

Long term visual localization has to conquer the problem of robust matching images with dramatic photometric changes caused by different seasons, weather conditions, natural and man-made illumination changes, etc. Due to the practical demand of applying visual localization at night, either for autonomous driving or for augmented reality related applications, how to extract keypoints and descriptors with robustness to day-night illumination changes on images has became the bottleneck. This paper proposes an adversarial learning based solution to harvest from the weakly domain labels of day and night images, along with the point level correspondences among day time images, to achieve robust local feature extraction and description across day-night images. The key idea is to learn a discriminator to distinguish whether a feature map is generated from the day image or night image, and simultaneously to adjust the parameters of feature extraction network so as to fool the discriminator. After adversarial training of the discriminator and feature extraction network, the feature extraction network finally reaches a stable status so that the extracted feature maps are robust to day-night photometric changes, based on which day-night domain invariant keypoints and descriptors can be extracted. Compared to existing local feature learning methods, it only requires an additional set of easily captured night images to improve the domain invariance of learned features. Experiments on two challenging day-night visual localization benchmarks show the effectiveness of the proposed method. In addition, this paper revisits the widely used image matching metrics on HPatches and finds that recall of different methods reflects their relative localization performance, while previously used mean matching accuracy is not essentially related to downstream tasks like visual localization.

Cross-Domain Few-Shot Hyperspectral Image Classification With Class-Wise Attention

Few-shot learning (FSL) is an effective method to solve the problem of hyperspectral image (HSI) classification with few labeled samples. It learns transferable knowledge from sufficient labeled auxiliary data to classify unseen classes with limited labeled samples for training. However, the distribution difference between auxiliary data and unseen classes results in the learned transferable knowledge not being well applied to the new task. Therefore, a class-wise attentive cross-domain FSL (CA-CFSL) framework is proposed in this article, in which a feature extractor is learned to extract data features with discriminability and domain invariance. The class-wise attention metric module (CAMM) introduces class-wise attention on the FSL framework to learn more discriminative features, which improves the interclass decision boundaries. Furthermore, an asymmetric domain adversarial module (ADAM) is designed to enhance the ability of extracting domain-invariant representations, which combines asymmetric adversarial training with embedded domain-specific information. Experimental results on four public HSI datasets demonstrate that the proposed method outperforms the existing methods.

An Effective Spectral Approach to Solving Fractal Differential Equations of Variable Order Based on the Non-singular Kernel Derivative

A new differential operators class has been discovered utilising fractional and variable-order fractal Atangana-Baleanu derivatives that have inspired the development of differential equations' new class. Physical phenomena with variable memory and fractal variable dimension can be described using these operators. In addition, the primary goal of this study is to use the operation matrix based on shifted Legendre polynomials to obtain numerical solutions with respect to this new differential equations' class, which will aid us in solving the issue and transforming it into an algebraic equation system. This method is employed in solving two forms of fractal fractional differential equations: non-linear and linear. The suggested strategy is contrasted with the mixture of two-step Lagrange polynomials, the predictor-corrector algorithm, as well as the fractional calculus methods' fundamental theorem, using numerical examples to demonstrate its accuracy and simplicity. The estimation error was proposed to contrast the results of the suggested methods and the exact solution to the problems. The proposed approach could apply to a wider class of biological systems, such as mathematical modelling of infectious disease dynamics and other important areas of study, such as economics, finance, and engineering. We are confident that this paper will open many new avenues of investigation for modelling real-world system problems.

Aligning Small Datasets Using Domain Adversarial Learning: Applications in Automated in Vivo Oral Cancer Diagnosis.

Deep learning approaches for medical image analysis are limited by small data set size due to factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, phases for system development and classification algorithms often overlap with data collection, creating small disjoint data sets collected at numerous locations with differing protocols. In this setting, merging data from different data collection centers increases the amount of training data. However, a direct combination of datasets will likely fail due to domain shifts between imaging centers. In contrast to previous approaches that focus on a single data set, we add a domain adaptation module to a neural network and train using multiple data sets. Our approach encourages domain invariance between two multispectral autofluorescence imaging (maFLIM) data sets of in vivo oral lesions collected with an imaging system currently in development. The two data sets have differences in the sub-populations imaged and in the calibration procedures used during data collection. We mitigate these differences using a gradient reversal layer and domain classifier. Our final model trained with two data sets substantially increases performance, including a significant increase in specificity. We also achieve a significant increase in average performance over the best baseline model train with two domains (p = 0.0341). Our approach lays the foundation for faster development of computer-aided diagnostic systems and presents a feasible approach for creating a robust classifier that aligns images from multiple data centers in the presence of domain shifts.

An Intelligent Fault Diagnosis Scheme for Rotating Machinery Based on Supervised Domain Adaptation With Manifold Embedding

In rotating machinery fault diagnosis, domain adaptation (DA) transfer learning-based framework has been attracting great attentions to tackle the problem of inconsistent feature distribution and insufficient labeled fault feature data. However, most of the existing approaches mainly focus on either the cross-domain distribution alignment or manifold subspace learning, which faces two critical limitations: 1) it is hard to overcome the feature distortions when aligning the distribution in the original feature space and 2) subspace learning is insufficient to decrease the distribution divergence. To address the above limitations, this work proposes an intelligent fault diagnosis scheme based on supervised DA with manifold embedding and key features selection. It first applies maximal overlap discrete wavelet packet transform (MODWPT) to process the vibration signals and performs the statistical feature extraction. In order to ensure that the selected key features are conductive to domain adaption, the fault discriminative ability and domain invariance of the features are investigated based on the domain differences and Laplacian score. Then, it presents a new supervised domain adaption with manifold embedding for the distribution alignment in manifold subspace by taking the class information and neighboring relationships into account. Finally, an intra classifier is learned to predict the unlabeled target domain. The proposed fault diagnosis scheme is evaluated using a set of practical data sets of motor and bearing. The extensive experimental results demonstrate that it significantly outperforms the comparative models and achieves much more effective fault diagnosis under different working conditions.

Evaluating and Improving Domain Invariance in Contrastive Self-Supervised Learning by Extrapolating the Loss Function

Evaluating and Improving Domain Invariance in Contrastive Self-Supervised Learning by Extrapolating the Loss Function

Mutual-assistance semisupervised domain generalization network for intelligent fault diagnosis under unseen working conditions

Generalizing deep models to unseen working conditions is an essential topic for intelligent fault diagnosis. Existing domain generalization-based fault diagnosis (DGFD) methods usually require sufficient annotated samples from all observed domains during the training phase, while annotating abundant samples is an expensive and difficult task. Therefore, this study proposes a mutual-assistance network for semisupervised domain generalization fault diagnosis (SemiDGFD), where only one source domain is labeled along with several unlabeled source domains. Reliable pseudo labels are assigned to unlabeled data with knowledge assistance from labeled data. Then, an entropy-based sample purification mechanism is designed to improve the quality of pseudo-labeled samples. Finally, pseudo-labeled samples cooperate with real-labeled samples to serve as the input of a low-rank decomposition, which discovers domain invariance against domain shift. Extensive diagnostic experiments demonstrate that the proposed method can obtain higher precision than other popular SemiDGFD methods and achieve comparable performance with up-to-date fully-labeled DGFD methods.

Joint Cross-Consistency Learning and Multi-Feature Fusion for Person Re-Identification

To solve the problem of inadequate feature extraction by the model due to factors such as occlusion and illumination in person re-identification tasks, this paper proposed a model with a joint cross-consistency learning and multi-feature fusion person re-identification. The attention mechanism and the mixed pooling module were first embedded in the residual network so that the model adaptively focuses on the more valid information in the person images. Secondly, the dataset was randomly divided into two categories according to the camera perspective, and a feature classifier was trained for the two types of datasets respectively. Then, two classifiers with specific knowledge were used to guide the model to extract features unrelated to the camera perspective for the two types of datasets so that the obtained image features were endowed with domain invariance by the model, and the differences in the perspective, attitude, background, and other related information of different images were alleviated. Then, the multi-level features were fused through the feature pyramid to concern the more critical information of the image. Finally, a combination of Cosine Softmax loss, triplet loss, and cluster center loss was proposed to train the model to address the differences of multiple losses in the optimization space. The first accuracy of the proposed model reached 95.9% and 89.7% on the datasets Market-1501 and DukeMTMC-reID, respectively. The results indicated that the proposed model has good feature extraction capability.

PENGARUH KESADARAN MERK DAN PERSEPSI KUALITAS TERHADAP KEPUTUSAN PEMAKAIAN JASA PENERBANGAN GARUDA INDONESIA

This study aims to determine the effect of brand awareness and perceived quality and to find out the attributes of brand awareness and perceived quality on the Garuda Indonesia airline on the departure route from Pontianak at Supadio Pontianak Airport which needs to be developed. This research was conducted for one month, using three dimensions of service usage decisions, namely Confidence in using services, Stability in using services, Conformity of attributes to needs and desires. The sample of this research is Garuda Indonesia airline passengers. The sample for this research used purposive sampling method. Data were collected using questionnaires and literature studies. The research method uses a quantitative research design in the form of a survey with classical assumption tests, multiple regression analysis and hypothesis testing. The results of this study show that the data is normally distributed with a significance level of 0.150 or > 0.05 and shows a unidirectional relationship between the independent variable and the dependent variable. It can be concluded that Garuda Indonesia airline passengers at Supadio Pontianak airport were satisfied with the brand and quality provided with a value (51.9%). Each variable dimension can be developed in order to increase brand awareness and perceived quality in this study.

PENGARUH KINERJA PENANGANAN DAN KUALITAS PELAYANAN BAGASI TERHADAP KEPUASAN PENUMPANG DI MASKAPAI CITILINK RUTE MALANG – JAKARTA

This study aims to determine the effect of handling performance and service quality and determine the attributes of handling performance and service quality on the Citilink Airlines Malang - Jakarta route at Abdurrachman Salech Airport Malang that need to be developed.
 This research was conducted for one month, using the dimensions of passenger satisfaction, namely professionalism, service responsiveness, service availability, overall satisfaction, comfort. The sample of this research is Citilink airline passengers. The sample in this study used purposive sampling method. Data was collected by using questionnaires and literature studies. The research method uses a quantitative research design in the form of a survey with Classical Assumption Test, Multiple Regression Analysis and Hypothesis Testing.
 The results of this study indicate that the data is normally distributed with a significance level of 0.282 or greater than 0.05, and indicates that there is a unidirectional relationship between the independent variable and the dependent variable. It can be concluded that Citilink airline passengers at Abdurrachman Saleh Airport Malang are satisfied with the handling performance and service quality provided with a value (69.6%). Each variable dimension can be developed to be able to improve the handling performance and quality of baggage services in this study.

The Relationship between Safety Leadership Style and Safety Climate in Phosphoric Acid Industry

Introduction: Phosphoric acid companies have risks that can lead to work accidents. There are several factors that influence the occurrence of work accidents, one of which is organizational and management factors. Leadership style is a factor in the work safety climate which leads to work accidents and work productivity. The purpose of this study was to analyze the relationship between leadership style and work safety climate in the Phosphoric Acid Plant at Phosphoric Acid Industry. Methods: The type of this research is descriptive quantitative research and through a cross-sectional approach to 44 workers at the Phosphoric Acid Plant. The variables of this study including safety leadership style and work safety climate. The data collection method used was secondary data obtained from the company’s annual data and as well as the primary data using the Leader Behavior Description Questionnaire and the Nordic Safety Climate Questionnaire. Each variable dimension was analyzed using the Spearman correlation method. Results: The results indicate that most workers, 47.7%, have both very high consideration and high initiating structure leadership style level. There is a relationship between leadership style and work safety climate with a correlation coefficient of 0.787 and shows a strong positive correlation. Conclusion: There is a strong positive relationship between leadership style and safety climate.

Variable Dimensional Scaling Method: A Novel Method for Path Planning and Inverse Kinematics

Traditional methods for solving the inverse kinematics of a hyper-redundant manipulator (HRM) can only plan the path of the end-effector with a complicated solving process, where obstacle avoidance is also not considered. To solve the above problems, a novel method for solving inverse kinematics of HRM is proposed in this paper: the variable dimension scaling method (VDSM), which can solve complex inverse kinematics while avoiding obstacles. Through this method, the path of the end-effector is scaled under a certain proportion and is adjusted depending on the position of the obstacle, which has good universality. The number of link angles changed is as small as possible in the process of achieving the end-effector moving along the desired path. With the redundancy of HRM, obstacle avoidance can be implemented in any environment by the proposed method. Through simulation and experiments in different environments, the above advantages of VDSM are verified.

Signal Modeling with IG Noise and Parameter Estimation Based on RJMCMC

Piecewise constant (PC) is a stochastic model that can be applied in various fields such as engineering and ecology. The stochastic model contains a noise. The accuracy of the stochastic model in modeling a signal is influenced by the type of noise. This paper aims to propose inverse-gamma noise in the PC model and the procedure for estimating the model parameters. The model parameters are estimated using the Bayes approach. Model parameters have a variable dimension space so that the Bayesian estimator cannot be determined analytically. Therefore, the Bayesian estimator is calculated using the reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm. The performance of the RJMCMC algorithm is validated using data synthesis. The finding is a new PC model in which the noise has an inverse-gamma distribution. In addition, this paper also proposes a parameter estimation procedure for the model based on an RJMCMC. The simulation study shows that the model parameter estimators generated by this algorithm are close to the model parameter values. This paper concludes that inverse gamma noise can be used as an alternative noise in the PC model. The RJMCMC is categorized as a valid algorithm and can estimate the PC model parameters where the noise has an inverse-gamma distribution. The novelty in this paper is the development of a new stochastic model and the procedure for estimating the model parameters. In application, the findings in this paper have the potential to improve the suitability of the stochastic model to the signal.