Art Methods Research Articles

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

AbstractNowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

Improving 3D Vulnerable Road User Detection With Point Augmentation

Point clouds have been a popular representation to describe 3D environments for autonomous driving applications. Despite accurate depth information, sparsity of pointsresults in difficulties in extracting sufficient features from vulnerable objects of small sizes. One solution is leveraging self-attention networks to build long-range connections between similar objects. Another method is using generative models to estimate the complete shape of objects. Both approaches introduce large memory consumption and extra complexity to the models while the geometric characteristics of objects are overlooked. To overcome this problem, this paper proposes Point Augmentation (PA)- RCNN, focusing on small object detection by generating efficient complementary features without trainable parameters. Specifically, 3D points are sampled with the guidance of object proposals and encoded through the 3D grid-based feature aggregation to produce localised 3D voxel properties. Such voxel attributes are fed to the pooling module with the aid of fictional points, which are transformed from sampled points considering geometric symmetry. Experimental results on Waymo Open Dataset and KITTI dataset show a superior advantage in the detection of distant and small objects in comparison with existing state-ofthe- art methods.

The significance of the special course "The Necklace of Russian Cities" as a holistic excursion direction in the educational process of higher education

The problem and the goal. Thematic excursions of various directions in modern education are an important communicative and cognitive area that makes the learning process fresh, emotionally receptive and has been acquiring special significance against the background of the general worldwide computer visualization. The purpose of the article is to analyze and reveal the significance of the special course "The Necklace of Russian Cities", as a whole area of educational activity, representing a cycle of educational excursions in order to offer it for implementation in higher education. Research methods. The following methods are used in the work: the historical and artistic method, which is directly focused on the historical study of architecture; a typological method that makes it possible to operate with a sufficient number of monuments in their cultural ties. A historical and cultural method aimed at determining the significance of Russian architecture of the 12th-20th centuries as a national artistic phenomenon in the history of art. Research results. A structural analysis of the special course "The Necklace of Russian Cities" has been carried out. It also includes an analysis of specific features (visibility, informativeness, scientific content, aesthetic orientation), which are especially distinct in art disciplines. The thematic aspects of the special course, which are significant in informative and educational terms, have been identified and outlined, in particular, the emphasis on the originality of Russian architecture in its cultural and historical ties, which is a phenomenon of a special kind. Conclusion. The introduction of the special course "The Necklace of Russian Cities" into the educational field will reveal the prospects for modern education on the path of interdisciplinary sciences, such as history, philosophy, and architecture. Here we can talk about the keen interest of students in visual forms of study that are truly communicative, related to the world outlook, especially in terms of sometimes forgotten global human values. Keywords: excursion, architecture, style, educational process, education, history, student

Reflections on the heuristic power of Contemplative Art in teaching and research

In this article I discuss the heuristic power of combining contemplative and creative practices using different stages of my research into learning through contemplation. This began with a realisation I had, in my first meditation retreat, about the similarities between creative and contemplative consciousness. It initiated twenty-three years of applied and theoretical research that started with contemplative art workshops I ran in rehabilitation centres. In PhD research that followed I tested a hypothesis of learning through contemplation founded on the concept of an elemental ground of learning that contained an integrating force I termed the feeling nexus. In later research examining the egress of meaning where practitioners return from contemplation, I discovered that art-making can act as a bridge between pre-conceptual knowing and cognitive assimilation of that knowledge. Recently I created the Meditative Process Art (MPA) method that engages the heuristic power of combining creative and contemplative practices.

A deep neural network based feature fusion panoramic segmentation method

Segmentation of complex and diverse scenes in the field of machine vision has the advantages of low cost and good robustness. However, in the real open intelligent driving scene, the segmented object will be affected by the change of proportion and illumination. The feature extraction of the interference algorithm causes some problems such as local information loss and feature background fusion in the process of execution. In this paper, deep neural network method is used for feature fusion panoramic segmentation. It focuses on the premise of lightweight, and improves the performance by improving the structure of semantic segmentation network layer and model calculation effect, and reducing the number of network parameters and calculations, so as to further improve the detection performance. At the same time, with the gradual deepening of deep learning network architecture, but most embedded devices can not bear a huge amount of computing workload. An attentional channel pruning based approach is proposed using a wide range of experimental and ablation studies to compare different data, including VOC2012 and ImageNet datasets. We show that the proposed method outperforms state of the art methods in terms of visual quality.

Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging and essential task that aims to retrieve a set of person images over visible and infrared camera views. In order to mitigate the impact of large modality discrepancy existing in heterogeneous images, previous methods attempt to apply generative adversarial network (GAN) to generate the modality-consisitent data. However, due to severe color variations between the visible domain and infrared domain, the generated fake cross-modality samples often fail to possess good qualities to fill the modality gap between synthesized scenarios and target real ones, which leads to sub-optimal feature representations. In this work, we address the cross-modality matching problem with Aligned Grayscale Modality (AGM), an unified dark-line spectrum that reformulates visible-infrared dual-mode learning as a gray-gray single-mode learning problem. Specifically, we generate the grayscale modality from the homogeneous visible images. Then, we train a style tranfer model to transfer infrared images into homogeneous grayscale images. In this way, the modality discrepancy is remarkably reduced in the image space. In order to reduce the remaining appearance discrepancy, we further introduce a multi-granularity feature extraction network to conduct feature-level alignment. Rather than relying on the global information, we propose to exploit local (head-shoulder) features to assist person Re-ID, which complements each other to form a stronger feature descriptor. Comprehensive experiments implemented on the mainstream evaluation datasets include SYSU-MM01 and RegDB indicate that our method can remarkably enhance cross-modality retrieval performance against the state of the art methods.

Assessment of Prediction Uncertainty Quantification Methods in Systems Biology.

Biological processes are often modelled using ordinary differential equations. The unknown parameters of these models are estimated by optimizing the fit of model simulation and experimental data. The resulting parameter estimates inevitably possess some degree of uncertainty. In practical applications it is important to quantify these parameter uncertainties as well as the resulting prediction uncertainty, which are uncertainties of potentially time-dependent model characteristics. Unfortunately, estimating prediction uncertainties accurately is nontrivial, due to the nonlinear dependence of model characteristics on parameters. While a number of numerical approaches have been proposed for this task, their strengths and weaknesses have not been systematically assessed yet. To fill this knowledge gap, we apply four state of the art methods for uncertainty quantification to four case studies of different computational complexities. This reveals the trade-offs between their applicability and their statistical interpretability. Our results provide guidelines for choosing the most appropriate technique for a given problem and applying it successfully.

Congestion Avoidance in Intelligent Transport Networks Based on WSN-IoT through Controlling Data Rate of Zigbee Protocol by Learning Automata

Congestion control is one of the primary challenges in improving the performance of wireless sensor networks (WSNs). With the development of this network based on the Internet of Things (IoT), the importance of congestion control increases, and the need to provide more efficient strategies to deal with this problem is strongly felt. This problem is even more important in applications such as Intelligent Transport Systems (ITSs). This article introduces a new method for congestion control in ITSs based on WSN-IoT infrastructure, namely, the Congestion Avoidance by Reinforcement Learning algorithm (CARLA). The purpose of the research was to improve the performance of the Zigbee protocol in congestion control through more efficient routing and also the intelligent adjustment of the data rate of the nodes. For this purpose, a topology control and routing strategy based on the multiple Bloom filter (MBF) is proposed in this research. Further, learning automata (LA) was used as a reinforcement learning model to adjust the data rate of network nodes in a distributed manner. These strategies distinguish the current research from previous efforts and can be effective in reducing the probability of congestion in the network. The performance evaluation results of the proposed algorithm in a simulated ITS environment were compared with conventional Zigbee and state of the art methods. According to the results, CARLA can improve PDR by 4.64%, and at the same time, reduce energy consumption and end-to-end delay by 11.44% and 25.26%, respectively. The results confirm that by using CARLA, in addition to congestion control in the ITS, energy consumption and the end-to-end delay can also be reduced.

КОМПОЗИЦИОННЫЕ ОСОБЕННОСТИ АРХИТЕКТУРЫ РУССКОГО СТИЛЯ ГОРОДА ВОРОНЕЖ

The article outlines the role and significance of the period of the Russian style in the historical development of the city of Voronezh. The stages of the development of the Russian style from the middle of the XIX – early XX centuries in the city of Voronezh and their characteristic regional features are considered. For the first time, a comprehensive analysis of the architectural buildings of the Russian style of Voronezh is carried out to identify the techniques of spatial composition, architectural planning solutions, compositional construction and artistic methods of facade design. Particular attention is paid to stylistic, compositional and morphological analyses of architectural details of building facades and decor. The research of the composite structure of facades is based on the scientific methodology developed by S.V. Sementsov, which allowed to analyze the facade construction system and present a comparative analysis of two periods of the development of the Russian style. The compositional principles of the axial construction of facades, the analysis of rhythm and meter, the breakdown of the facade into horizontal and vertical divisions, the location of plastic decor and the choice of color solutions are described. Then, on the basis of the theory of architectural forms, a morphological analysis of the architectural decor of buildings was carried out: crowning elements, window and door frames. The characteristic facade forms for the architecture of the Russian style of Voronezh are revealed, the variants of their layout and the varieties of building materials used are analyzed.

One text, two worlds, third space: Design principles for bridging the two-worlds divide in teacher education

Teacher educators have designed innovative interventions to bridge the damaging divide between university and P-12 experiences by creating hybrid spaces where participants and ideas interact on common ground. Based on our analysis of these interventions, we propose three design principles for teacher education: shared settings, shared roles, and shared texts. We used these principles to design and study a university English Language Arts methods course. Analysis of interviews, classroom discussion, student teacher evaluations, and other data indicate that together, the three principles support a third space for participants to share expertise and break down hierarchies, integrate theory and practice, and build community.

A Secure Scheme for Network Coding with Deep Learning in Industrial Internet of Things

This paper proposes a network coding scheme that utilizes neural network coding and steganography technique for Industrial Internet of Things (IIoT). This proposal gives an efficient error-correcting and secure network coding strategy simultaneously for general non-multicast network for the first time. The trivial concatenation of the above two schemes will not work, and we adopt a smart skill to fuse the neural network coding scheme and the steganography scheme seamlessly. The whole neural network model includes the network coding part and the steganography part. The first skill is making the network coding part converge in advance, and then we make the steganography part converged. The second skill is delicately designing the neural network structure and parameters to make the whole model work efficiently. The new proposal can correct more errors and confront more eavesdroppers than state of the art method in network coding. Besides, the transmission information rate is three, which is bigger than one, which is the most information rate of traditional channel code based on Shannon theory. We use a steganography neural network model to encode three images into one image and at last, the three images can be recovered simultaneously. The new scheme will give a practical error-correcting and secure network coding strategy, and then decrease the energy consume in the communication, which will benefit IIoT.

RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning

The installed wind power capacity is growing worldwide. Remote condition monitoring of wind turbines is employed to achieve higher up-times and lower maintenance costs. Machine learning approaches can be used for detecting developing faults in wind turbines in their earlier occurrence. However, training fault detection models may require large amounts of past and present data. These data are often not available or not representative of the current operation behaviour. These data can be acquired with supervisory control and data acquisition systems. Note also that newly commissioned wind farms lack data from previous operation, whilst older installations may also lack representative working condition data as a result of control software updates or component replacements. After such events, a turbine’s operation behaviour can change significantly so its data are no longer representative of its current behaviour. Therefore, this paper shows that cross–turbine transfer learning can improve the accuracy of fault detection models in turbines with scarce data from supervisory control and data acquisition systems. In particular, it highlights that combining the knowledge from turbines with scarce data and turbines with plentiful data enables earlier detection of faults than prior art methods. In this way, the reuse and the knowledge transfer across wind turbines allows us to overcome this lack of data, thus enabling accurate fault detection in wind turbines.

ART PROJECTS AS HUMANITARIAN STRATEGIES FOR PSYCHOSOCIAL SUPPORT: INTERNATIONAL EXPERIENCE OF DEVELOPMENT AND IMPLEMENTATION

The purpose of the article is to substantiate the possibilities of using the international experience in the field of cultural and artistic practices as a humanitarian strategy for psychosocial support for children and youth in Ukraine under martial law, based on a cultural analysis of the implementation of the «Mobile Arts for Peace» (MAP) art project. The research methodology is to use interdisciplinary and comparative approaches to study the specifics of the therapeutic impact of various types of arts – theatre, music, visual arts, choreography in the implementation of international art projects in post-conflict societies. The main research methods are: the method of hermeneutic analysis – for the interpretation of works of art created within certain national cultures; the method of semiotic analysis –for revealing the meanings and values of artistic phenomena as sign systems; the method of psychological experiment – for analysing the results of using innovative artistic methods in the process of psychological support for project participants. The scientific novelty consists in summarising the international experience of developing and implementing the «Mobile Arts for Peace» art project in different countries of the world – Rwanda, Nepal, Indonesia, Kyrgyzstan; substantiating the possibilities of providing psychosocial support to participants in art projects as forms of cultural and artistic practices; identifying the features of the MAP project implementation based on the specifics of national cultural traditions, historical, and cultural memory, and the depth of cultural trauma in each society. Conclusions. The analysis of the process of implementing the «Mobile Arts for Peace» art project allowed us to summarise the international experience of engaging art as an effective tool for peacebuilding, establishing dialogue, and providing psychological assistance to societies in countries that have gone through various types of conflicts. The studied international experience can be successfully used to develop concepts, programs, and methodological recommendations adapted to the needs of Ukrainian society in the context of war.
 Key words: international art project, Mobile Arts for Peace, cultural and artistic practices, psychosocial support, humanitarian strategies.

The influence of concomitant extragenital pathology on the success of assisted reproductive technology procedures in women (according to clinics of the Volgograd region)

At the moment, the problem of infertility does not lose its relevance. In the structure of this disease, the violation of reproductive function in women is dominant. The present study is devoted to the study of the influence of concomitant extragenital pathology on the success of assisted reproductive technology procedures. The data of 184 women who underwent infertility treatment by ART methods in clinics of the Volgograd region in the period from 2015 to 2020 were analyzed. The following parameters of the patients were studied: age, gynecological history, the main cause of infertility, transferred diseases of non-productive organs, the protocol of ovarian stimulation with gonadotropins, the number of mature follicles by the end of stimulation, the number of received, fertilized eggs, the number of embryos by day 3, 5. It has been established that concomitant extragenital pathology affects the processes of folliculogenesis and qualitatively affects oogenesis.

Perception of Visual Images in the Psychology of Art

The article presents a theoretical analysis of the phenomenology of visual perception in the psychology of fine art. Aspects of perception as one of the mental functions and visual images as meaningful perceptual phenomena are analyzed. The question of the predestination of perception, its development and foundations is discussed. With the help of the analysis of many fundamental sources, the main characteristic of visual perception, influence on modern culture and human thinking has been established. The possibilities of applying the psychology of art in psychological counseling are described. The practical methods of fine art are explained from the standpoint of psychology, as well as examples of the perception of visual images in art therapy based on the research of domestic and foreign authors are indicated. In conclusion, examples of human perception of visual images in psychodiagnostic techniques are considered, and prospects for future theoretical and practical work in the field of psychotherapy through the prism of fine art are outlined.

FERFM: An Enhanced Facial Emotion Recognition System Using Fine-tuned MobileNetV2 Architecture

Deep learning techniques have recently drawn a lot of attention towards automatic facial emotion recognition (FER) applications such as diagnosis of psychiatric disorders, mental health monitoring, and other human–computer interaction applications. But the models that have been developed, have still not been generalized due to the absence of substantial emotion datasets and high operational costs. To address these issues, we propose a novel technique called facial emotion recognition using fine-tuned MobileNetV2 architecture (FERFM). It uses the concept of transfer learning to improve the performance of FER systems for mobile devices. It transfers the knowledge from pre-trained ImageNet dataset to profile view RGB-KDEF dataset for diversity among learnt features. Initially, data descriptive analysis is performed on input KDEF images to automate the necessary knowledge. Then the pipeline strategy is introduced, where the pre-trained MobileNetV2 architecture is fine tuned by eliminating the last six layers and adding dropout regularization, max pooling and dense layer. To test the performance of the proposed work, we ran several tests using the KDEF-RGB dataset, FER13 dataset and real-time facial expression images to classify the results into seven discrete categories of emotions. It was also verified on VGG-16 pretrained model and demonstrated remarkable performance comparatively. Proposed FERFM achieves 85.7% accuracy and requires 1,510,599 number of trainable parameters, 43 ms running time per image that proves the enhancement of FER performance and outperforms the existing state of art methods. Also, the ability to achieve low operational costs while maintaining high accuracy demonstrates proficiency in mobile applications.

Optimal multi-kernel SVM classifier with rotation, illumination and scale invariant hybrid DWT-Shearlet based GLCM feature descriptor and its application to face recognition

In computer vision, we must handle with the various structural aspects of image or video data. The texture is one of the most important aspects of this type of data, which is utilised to identify objects or regions of interest in an image. As imaging conditions change, textures inside actual images significantly change in brightness, contrast, size, and skew. To recognise textures in real-world images, a similarity measure that is invariant to these features must be used. Existing recognition techniques did not perform well due to issues such as illumination, scale, and subject rotation. To address this issue, invariant feature representation methods are being developed to generate features that are insensitive to such variations. In this paper, we proposed a robust hybrid feature descriptor and predicted the faces under illumination, scale, and pose variations using an optimum multi-kernel support vector machine. Additionally, the suggested robust hybrid feature descriptor is enhanced by combining a hybrid transform composed of discrete wavelet and discrete shearlet transforms with some image statistical textural data. The proposed face recognition system is implemented in MATLAB, and analysed using various parameters to show proposed methods improved performance as compared to the state of the art methods.

CADNet: an advanced architecture for automatic detection of coronary artery calcification and shadow border in intravascular ultrasound (IVUS) images.

Intravascular Ultrasound (IVUS) is a medical imaging modality widely used for the detection and treatment of coronary heart disease. The detection of vascular structures is extremely important for accurate treatment procedures. Manual detection of lumen and calcification is very time-consuming and requires technical experience. Ultrasound imaging suffers from the generation of artifacts which obstructs the clear delineation among structures. Considering, the need, to provide special attention to crucial areas, convolutional block attention modules (CBAM) is integrated into an encoder-decoder-based U-Net architecture along with Atrous Spatial Pyramid Pooling (ASPP) to detect vessel components: lumen, calcification and shadow borders. The attention modules prove effective in dealing with areas of special attention by assigning additional weights to crucial channels and preserving spatial features. The IVUS data of 12 patients undergoing the treatment is taken for this study. The novelty of the model design is such that it is able to detect the lumen area in the presence/absence of calcification and bifurcation artifacts too. Also, the model efficiently detects the calcification area even in case of severely complex lesions with shadows behind them. The main contribution of the work is that IVUS images of varying degrees of calcification till 360° are also considered in this work, which is usually neglected in previous studies. The experimental results of 1097 IVUS images of 12 patients resulted in meanIoU (0.7894 ± 0.011), Dice Coefficient (0.8763 ± 0.070), precision (0.8768 ± 0.069) and recall (0.8774 ± 0.071) of the proposed model CADNet which show the model's effectiveness relative to other state-of-the art methods.

Joint autoencoder-regressor deep neural network for remaining useful life prediction

Ubiquitous availability of IoT technologies allows processing of large amounts of data to improve prognostics tasks in industrial applications. One such important task of prognostics is the prediction of remaining useful life of a system from past performance data. In practice, although failure points are pinpointed, the actual start of degradation is not necessarily available, but usually modeled simply with a linear degradation assumption. In this paper we present a data-driven approach to remaining useful life prediction using joint autoencoder-regression network, a deep neural network model incorporating a convolutional neural network autoencoder and a long-short term memory network regressor trained end-to-end. We also present a new fault detection-based approach to modeling remaining useful life degradation. This model allows a better estimate of the start and progress of equipment degradation ending with a failure. We demonstrate the effectiveness of the proposed algorithms on two datasets. The first one is C-MAPSS frequently used as a benchmark among prognostic researchers. The second one is PHME20, a recent prognostic dataset from a prognostics competition. These experiments show that the proposed algorithms are capable of predicting remaining useful life as good as the state of art methods. The results also show that fault detection-based labeling outperforms linear labeling.

Classifications of benign and malignant mammogram images using Gabor‐modified CNN architecture

AbstractBreast cancer is one of the leading life killing cancers in women patients around the world. Digital mammogram is used to detect and segment the abnormal mass portions in breast region. In this article, the breast cancer regions are detected and segmented using the Gabor transform based Convolutional Neural Networks (CNN) classification approach. This proposed breast cancer detection method consist of the following modules Kirsch's edge detector, Gabor transform, CNN classification and Segmentation. The cancer pixels and healthy pixels are differentiated in edge pixels. Hence, the Kirsch's edge detector is used to detect the fine edge boundary pixelsthe in source mammogram image. Then, the spatial edge detected mammogram image is converted into multi resolution mammogram image using Gabor transform. This transformed multi resolution mammogram image is classified into Normal, Benign or Malignant in this article using the proposed CNN architecture. The developed CNN structure extracts more complex feature maps from their internal layers to improve the classification rate. The conventional and proposed CNN structure is differed with respect to the internal layers. The proposed CNN structure is optimized by reducing the usage of the internals and increases Convolutional Filters (CF) in each Convolutional Layer during utilization design process. After classification process over, the cancer regions in the abnormal mammogram images are segmented using morphological functions. The proposed breast cancer detection is evaluated on Mammographic Image Analysis Society (MIAS) dataset and their cancer region segmentation results are 98.4% of sensitivity, 98.9% of specificity and 99.2% of cancer region segmentation accuracy. The average Detection Rate (DR) of the proposed breast cancer detection method is about 98.3% on the set of mammogramthe images from MIAS dataset. These simulation results are compared with other state‐of‐the art methods and cross verified by the k‐fold verification algorithm.