Concept Of Network Research Articles

Conception of robust neural networks to improve hybrid control of an induction motor

Neural networks and fuzzy controllers are considered as the most efficient approximators of different functions and have also proved their capability of controlling nonlinear dynamical systems. So, in this paper, the authors introduce a novel technique of control called ‘hybrid control’ which is Based on Feedback Linearization and Field Oriented Control of an Induction Motor, in order to replace the sliding mode controllers (speed and flux ones). In fact, the objectives required by the introduction of neural networks, ‘RANNCs’ is to perform the control which is shown by simulation results.

Automatic classification of breeds of dog using convolutional neural network

Dog is a mammal that has been a friend of man for ages, it is naturally a domestic animal with a high level of phenotype differences in behaviour and morphology. Breeding and crossbreeding activities have increased the number of dog breeds globally, thereby resulting in dogs with inter breed similarities and intra breed differences thereby creating a difficulty in their classification. The American Kennel Club (AKC) classified breeds of dog into groups based on characteristic, purpose, behaviuor and uses in order to optimize the potentials in the breeds. However, most people find it difficult to identify and classify the dog breed groups. Existing works did not consider the automatic grouping of dog breeds. Hence, there is need for automatic techniques to classify dog breeds into groups with improved accuracy. This work used the concept of Convolutional Neural Network (CNN) to develop a model that will automatically classify dog breeds into group based on the American Kennel Club standard using the Stanford’s dog dataset. The developed model achieved 92.2% accuracy, 80.0% sensitivity, 95.3% specificity and 93.4% area under curve (AUC). The model’s performance is excellent compared to existing works that used the same dataset. The experimental result was validated with two classic CNN models (ResNet-50 and SqueezeNet) using the same parameters.

Advanced monitoring and numerical modelling of the stability, safety and reliability indicators of the earthen dam of Songloulou (Cameroon).

For the determination of global stability after long term advanced monitoring, artificial intelligence have been used for the data analysis of water level and displacements of Songloulou earth dam at Cameroon. Measurements of safety and reliability indicators follow changes set by piezometric and pendulums measurements. The results obtained from the artificial intelligence on the base of many years recording data have confirmed the relevance and robustness of this model. The ANFIS model combining the concept of neural network and fuzzy logic was used to simulate the behaviour of piezometers and pendulums in the dam. This model has provided satisfactory results, given in the large amount of data to be processed. The water level evolution is modelled using the ANFIS function integrated in the MATLAB software and the result is compared to that obtained by the HST method. Afterwards, the state of stress on the structure and stability of the slope at shear have been assessed based on the hydro mechanical behaviour using the GEOSTUDIO Finite Element computation software. The input parameters are: the head of water recorded in the piezometers and geotechnical parameters of the dam. The modelling results in terms of displacement are accurately consistent with the displacement measurements. The horizontal displacement of pendulums obtained by GEOSTUDIO is 80 mm and those measured directly of the pendulums have 70 mm of average value. The safety factor for slope stability according to 530 m water level is 1.5.

A Review on Efficient Energy Consumption in Software-Defined Networking Using Routing Aware Protocols

The concept of Software-Defined Networking (SDN) has been a fascinating and growing interest in the field of research. The programmable network component is allowed by the SDN’s promising characteristics and partitions the control plane together with the forwarding plane. Energy Efficiency (EE) turned out to be a vital design requisite for modern networking mechanisms since the energy costs supply hugely to the entire costs in networks. Nevertheless, as it is necessary to handle the trade-off betwixt EE and Network Performance (NP), designing energy-effective solutions is non-trivial. Thus, by utilizing Energy-Aware Routing (EAR) approaches, this paper reviews the methodologies of Energy Consumption (EC) on SDN. The latest research related to the traffic-aware solution, compacting TCAM solution, end-host aware solutions, rule placement solutions, heuristic approach-centric solution, and EAR routing protocol was highlighted by this review article in terms of optimal EC on SDN. Finally, centered on the EC metrics, the current research methodologies’ performance is assessed in the performance evaluation. By utilizing EAR routing, this type of research is helpful for future research in efficient EC in SDN.

Kunigaikščio Margirio pėstininkų bataliono veiklos viešinimo socialiniuose tinkluose analizė

The article analyses the publicity of the activities of the statutory organization in social networks. The theoretical part discusses the concept of activity publicity, the concept of social networks, introduces the activities of the Lithuanian army and the regulation of its publicity. Empirical qualitative research revealed that the activities of the Duke Margiris Infantry Battalion are publicized on Facebook and Instagram. The collected public posts were analysed according to three criteria: the number of likes, comments and shares of the posts. According to the content of the posts, four themes were distinguished: "combat training", "commemorations/formations", "civic education" and "visits". The obtained research results imply recommendations: in the case of the possibility of providing more visual content in the posts; to use the functions of the social network Facebook to create messages about upcoming events ("event", "story") and to remind about upcoming events by sharing messages. Keywords: publicity of military activities, publicity channels and means, social networks, publicity content and methods, feedback.

Navigating Governance Networks for Urban Issues: Global Trends and Prospects

This study examines the critical role of governance networks in addressing urban issues from a global perspective. We further elucidate the inception of these networks and how they can contribute to resolving the multifaceted challenges in urban issues. We employed a qualitative research method underpinned by an integrated literature review (ILR) approach. ILR, as a research methodology, allows for the critical examination, integration, and synthesis of extant literature, thereby enabling the generation of fresh frameworks and perspectives on the topic at hand. Our findings underscore the need for diverse actors, including government officials, societal figures, and entrepreneurs, to govern these networks and address urban issues. We also discovered that governance networks, by structuring societal dynamics, are systematically influenced by global socioeconomic and political regulatory mechanisms. Their applications span various public sector issues, such as mass media, healthcare, and water governance. The primary limitation of our study lies in the lack of field studies. Despite this, the outcomes offer significant insights into the theoretical concepts of governance networks from different regions, a distinctive contribution to the existing body of knowledge.
 Keywords: urban issues, governance networks, theoretical concept

How far is my network from being edge-based? Proximity measures for edge-basedness of unrooted phylogenetic networks

Phylogenetic networks which are, as opposed to trees, suitable to describe processes like hybridization and horizontal gene transfer, play a substantial role in evolutionary research. However, while non-treelike events need to be taken into account, they are relatively rare, which implies that biologically relevant networks are often assumed to be similar to trees in the sense that they can be obtained by taking a tree and adding some additional edges. This observation led to the concept of so-called tree-based networks, which recently gained substantial interest in the literature. Unfortunately, though, identifying such networks in the unrooted case is an NP-complete problem. Therefore, classes of networks for which tree-basedness can be guaranteed are of the utmost interest. The most prominent such class is formed by so-called edge-based networks, which have a close relationship to generalized series–parallel graphs known from graph theory. They can be identified in linear time and are in some regards biologically more plausible than general tree-based networks. While concerning the latter proximity measures for general networks have already been introduced, such measures are not yet available for edge-basedness. This means that for an arbitrary unrooted network, the “distance” to the nearest edge-based network could so far not be determined. The present manuscript fills this gap by introducing two classes of proximity measures for edge-basedness, one based on the given network itself and one based on its so-called leaf shrink graph (LS graph). Both classes contain four different proximity measures, whose similarities and differences we study subsequently.

Optimization of Channel Segregation-Based Fractional Frequency Reuse for Inter-Cell Interference Coordination in Cellular Ultra-Dense RAN

To cope with ever growing mobile data traffic, we recently proposed a concept of cellular ultra-dense radio access network (RAN). In the cellular ultra-dense RAN, a number of distributed antennas are deployed in the base station (BS) coverage area (cell) and user-clusters are formed to perform small-scale distributed multiuser multi-input multi-output (MU-MIMO) transmission and reception in each user-cluster in parallel using the same frequency resource. We also proposed a decentralized interference coordination (IC) framework to effectively mitigate both intra-cell and inter-cell interferences caused in the cellular ultra-dense RAN. The inter-cell IC adopted in this framework is the fractional frequency reuse (FFR), realized by applying the channel segregation (CS) algorithm, and is called CS-FFR in this paper. CS-FFR divides the available bandwidth into several sub-bands and allocates multiple sub-bands to different cells. In this paper, focusing on the optimization of the CS-FFR, we find by computer simulation the optimum bandwidth division number and the sub-band allocation ratio to maximize the link capacity. We also discuss the convergence speed of CS-FFR in a cellular ultra-dense RAN.

Soft skills centrality in graduate studies offerings

ABSTRACT Is it possible to measure how critical soft skills like leadership or teamwork are from the viewpoint of graduate studies offerings? This paper provides a conceptual and methodological framework that introduces the concept of a bipartite network as a practical way to estimate the importance of soft skills as socio-emotional abilities trained in graduate studies. We examined 230 graduate programs offered by 49 higher education institutions in Colombia to estimate the empirical importance of soft skills from the viewpoint of graduate studies offerings. The results show that: (a) graduate programs in Colombia share 31 soft skills in their intended learning outcomes; (b) the centrality of these skills varies as a function of the graduate pro- gram, although this variation was not statistically significant; and (c) while most central soft skills tend to be those related to creativity (i.e. creation or generation of ideas or projects), leadership (to lead or teamwork), and analytical orientation (e.g. evaluating situations and solving problems), less central were those related to empathy (i.e. understanding others and acknowledgment of others), ethical thinking, and critical thinking, posing the question if too much emphasis on most visible skills might imply an unbalance in the opportunities to enhancing other soft skills such as ethical thinking.

Multi-criteria dispatch optimization of a community energy network with desalination: Insights for trading off cost and security of supply

The idea of community energy network is being advocated to enhance the elasticity of diverse energy systems required for efficiently integrating a substantial volume of distributed energy resources. On the other hand, the interest in renewables-based desalination systems has received significant interest recently to consider freshwater as an additional end-use product in the community energy network system. Within this context, this paper introduces a multifaceted method for community energy networks with a focus on desalination-capable systems. The central goals involve diminishing the cumulative long-term expenses of the configuration, all while concurrently augmenting the system's capacity to store electrothermal energy on a daily basis that varies – all aimed at enhancing the reliability and security of resource provisioning. Importantly, the model co-optimizes the community energy network expenditure and reserve capacities, whilst integrating electrical, thermal, and natural gas vectors, as well as providing a platform for supplying freshwater needs. The overall freshwater provisioning infrastructure incorporates a water storage system, a desalination unit, a water well component, and a water pumping system. Furthermore, for the purpose of enhancing the adaptability, the community energy network concept put forth here utilizes coordinated electrothermal responsive load initiatives. These are coupled with meticulously planned electrothermal reservoir setups to curtail the wastage of surplus renewable production amidst diverse origins of unpredictability. The normalized weighted sum method is employed to convert the proposed formulation to a single-objective problem that is amenable to commercially available solvers in GAMS software. Then, the modelling framework is adapted to a system populated for a hypothetical site. The results verify the validity of the model in yielding globally optimum results for complex community energy networks with intertwined vectors of energy and end-use products. They also indicate that relatively small raises in the size of the electric and thermal reservoirs – and insubstantial raises in the expenditure of the system – can have potentially significant impacts on the ability of the system in serving loads during contingency conditions. In particular, by implementing demand response programs a cost reduction of 2.07% is shown, which is significant in the day-ahead operational planning phase.

FILNet: Fast Image-Based Indoor Localization Using an Anchor Control Network.

This paper designs a fast image-based indoor localization method based on an anchor control network (FILNet) to improve localization accuracy and shorten the duration of feature matching. Particularly, two stages are developed for the proposed algorithm. The offline stage is to construct an anchor feature fingerprint database based on the concept of an anchor control network. This introduces detailed surveys to infer anchor features according to the information of control anchors using the visual-inertial odometry (VIO) based on Google ARcore. In addition, an affine invariance enhancement algorithm based on feature multi-angle screening and supplementation is developed to solve the image perspective transformation problem and complete the feature fingerprint database construction. In the online stage, a fast spatial indexing approach is adopted to improve the feature matching speed by searching for active anchors and matching only anchor features around the active anchors. Further, to improve the correct matching rate, a homography matrix filter model is used to verify the correctness of feature matching, and the correct matching points are selected. Extensive experiments in real-world scenarios are performed to evaluate the proposed FILNet. The experimental results show that in terms of affine invariance, compared with the initial local features, FILNet significantly improves the recall of feature matching from 26% to 57% when the angular deviation is less than 60 degrees. In the image feature matching stage, compared with the initial K-D tree algorithm, FILNet significantly improves the efficiency of feature matching, and the average time of the test image dataset is reduced from 30.3 ms to 12.7 ms. In terms of localization accuracy, compared with the benchmark method based on image localization, FILNet significantly improves the localization accuracy, and the percentage of images with a localization error of less than 0.1m increases from 31.61% to 55.89%.

Ground Truth Validation of Sentinel-2 Data Using Mobile Wireless Ad Hoc Sensor Networks (MWSN) in Vegetation Stands

Satellite-based remote sensing (RS) data are increasingly used to map and monitor local, regional, and global environmental phenomena and processes. Although the availability of RS data has improved significantly, especially in recent years, operational applications to derive value-added information products are still limited by close-range validation and verification deficits. This is mainly due to the gap between standardized and sufficiently available close-range and RS data in type, quality, and quantity. However, to ensure the best possible linkage of close-range and RS data, it makes sense to simultaneously record close-range data in addition to the availability of environmental models. This critical gap is filled by the presented mobile wireless ad hoc sensor network (MWSN) concept, which records sufficient close-range data automatically and in a standardized way, even at local and regional levels. This paper presents a field study conducted as part of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN), focusing on the information gained with respect to estimating the vegetation state with the help of multispectral data by simultaneous observation of an MWSN during a Sentinel-2A (S2A) overflight. Based on a cross-calibration of the two systems, a comparable spectral characteristic of the data sets could be achieved. Building upon this, an analysis of the data regarding the influence of solar altitude, test side topography and land cover, and sub-pixel heterogeneity was accomplished. In particular, variations due to spatial heterogeneity and dynamics in the diurnal cycle show to what extent such complementary measurement systems can improve the data from RS products concerning the vegetation type and atmospheric conditions.

SPACNet: A Simulation Platform of an Acoustic Cognitive Network

Originating from the concept of cognitive networks (CNs), which are becoming popular in wireless terrestrial communication scenarios, underwater acoustic cognitive networks (UACNs) are drawing more and more attention in the field of the Internet of Underwater Things (IoUT). However, as the implementation of cognitive mechanisms in underwater acoustic networks is different from that of wireless scenarios, it is impossible or difficult for traditional simulation platforms to carry out simulations of UACNs. There is a lack of specialized simulation tools in terms of UACNs. To enable the quantitative evaluation of the effectiveness and performance enhancement of a UACNs in an adverse underwater environment, a simulation platform of acoustic cognitive networks (SPACNet) was designed and investigated in this article. First, based on a state machine-based protocol programming framework, the SPACNet is capable of supporting the implementation of different state-transform types associated with cognitive networking protocols. Moreover, to facilitate the realization of cognitive function at comprehensive levels of signal, information, and link, an underwater acoustic channel model with an environmental parameter input is integrated in SPACNet to generate underwater environment-driven multiple-aspect behaviors. Moreover, a simplified collision model consisting of an environment factor, channel response, and node location is used to reduce the complexity of the simulation of UACNs signal reception. A simulation was carried out to verify the effectiveness of SPACNet in evaluating the cognitive capabilities of UACNs. Finally, a field UACNs experiment was performed to validate the general consistency between the conclusion obtained with the SPACNet-based simulation and that from the field test.

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study’s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond.

Exploring the Interdependencies Between Transportation and Stormwater Networks: The Case of Norman, Oklahoma

The significance of critical infrastructure systems in maintaining productivity is undeniable. However, such systems remain susceptible to external disturbances and cascading failures. Instead of operating independently, these physical systems, such as transportation and stormwater systems, form an interdependent system. This interdependence, particularly important during flooding, illustrates that the failure of a stormwater system can disrupt traffic networks. To explore the extent of such interdependency, this study investigates the transportation and stormwater networks in Norman, Oklahoma. Using network science theories and concepts of multilayered networks, this paper analyzes these systems, both individually and in combination. The study identifies closely located components in the road and stormwater networks using Moran's I spatial autocorrelation metric. Next, the connectivity of these networks is represented in a graph format to investigate the topological credentials (i.e., rank of relative importance) of the network components (i.e., water inlets, road intersections as nodes, and stormwater conduits, road segments as links). Moreover, such credentials further change by considering the weights of the network components (i.e., average daily traffic, water flow). The proximity-based connectivity considerations between these networks utilizing Moran's I significance score revealed a good indicator of spatial interdependency. When incorporating directionality, the multilayer network analysis highlights that highly central components tend to cluster spatially, unlike the undirected counterpart. The study also identifies vulnerable locations and network components in a combined network setting that differ from the networks in isolation. In doing so, the research reveals new insights governing the complex reliance of transportation systems on neighboring stormwater systems.

Effects of Aging on Successful Object Encoding: Enhanced Semantic Representations Compensate for Impaired Visual Representations.

Although episodic memory and visual processing decline substantially with healthy aging, semantic knowledge is generally spared. There is evidence that older adults' spared semantic knowledge can support episodic memory. Here, we used functional magnetic resonance imaging (fMRI) combined with representational similarity analyses (RSAs) to examine how novel visual and preexisting semantic representations at encoding predict subjective memory vividness at retrieval. Eighteen young and seventeen older adults (female and male participants) encoded images of objects during fMRI scanning and recalled these images while rating the vividness of their memories. After scanning, participants discriminated between studied images and similar lures. RSA based on a deep convolutional neural network and normative concept feature data were used to link patterns of neural activity during encoding to visual and semantic representations. Relative to young adults, the specificity of activation patterns for visual features was reduced in older adults, consistent with dedifferentiation. However, the specificity of activation patterns for semantic features was enhanced in older adults, consistent with hyperdifferentiation. Despite dedifferentiation, visual representations in early visual cortex (EVC) predicted high memory vividness in both age groups. In contrast, semantic representations in lingual gyrus (LG) and fusiform gyrus (FG) were associated with high memory vividness only in the older adults. Intriguingly, data suggests that older adults with lower specificity of visual representations in combination with higher specificity of semantic representations tended to rate their memories as more vivid. Our findings suggest that memory vividness in aging relies more on semantic representations over anterior regions, potentially compensating for age-related dedifferentiation of visual information in posterior regions.SIGNIFICANCE STATEMENT Normal aging is associated with impaired memory for events while semantic knowledge might even improve. We investigated the effects of aging on the specificity of visual and semantic information in the brain when viewing common objects and how this information enables subsequent memory vividness for these objects. Using functional magnetic resonance imaging (fMRI) combined with modeling of the stimuli we found that visual information was represented with less specificity in older than young adults while still supporting memory vividness. In contrast semantic information supported memory vividness only in older adults and especially in those individuals that had the lowest specificity of visual information. These findings provide evidence for a spared semantic memory system increasingly recruited to compensate for degraded visual representations in older age.

Context based diversification on keyword search by conceptualization of typed terms of the query

The human era is evolved and dominated through the ultimate intention to know about the Universe and its assets more and more. This in turn persuaded him to gather the immense information of need in the form of theory, tools, intuitions, visuals, and ultimately as the form of abstract objects. In modern Information practice, the art of Conceptualization and the induction of the data driven paradigm identifies the user query intention over a Conceptual network to mold a confident search segment of user choice. The proposed Conceptualization with Typed Terms of Query (CTTQ) approach derives a confident segment of the user query which is contextually measured by means of novel randomized Machine learning algorithms and its surrogates. The Normalized Discounted Cumulative Gain (nDCG) measures of the CTTQ show the usefulness of the query suggestions. It's efficiency improvements are represented with varying impact lexical feature indicator (θ) values.

MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction With Neural ODEs

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.

Collateral network concept in 2023.

Extensive thoracoabdominal aortic aneurysm repair can cause spinal cord ischemia which significantly impacts survival and quality of life. Although this complication is uncommon, it is important to recognize the pathophysiology and preventative measures. In the 1990s, Dr. Griepp and colleagues proposed the existence of an extensive collateral network that supports spinal cord perfusion, "the collateral network concept". This includes an interconnecting complex of vessels in the intraspinal, paraspinous, and epidural spaces, and in the paravertebral muscles, involving the intercostal and lumbar segmental arteries as well as the subclavian and hypogastric (iliac) arteries. In this concept, as opposed to the one major segmental input model such as the Adamkiewicz artery, recognition of the importance of multiple inputs to the spinal circulation is paramount to maintaining the spinal blood flow and preventing spinal cord ischemia. In this article, we review the current evidence of the collateral concept and its application in aortic surgery.

Reengineering of Micro-Merchandising as a Business Process Using Neurotechnologies

The article presents a study of merchandising as a business process. The main problems in micro-merchandising are revealed. The expediency of using artificial intelligence technologies in micro-merchandising to increase its economic efficiency is substantiated. An approach to the reengineering of micro-merchandising based on a comparison of a planogram and a realogram using artificial intelligence (AI) technologies is proposed. The evolutionary stages of development of neurotechnologies are determined: from the formalization of the concept of a neural network and the representation of complex biological processes by mathematical models to the achievement of the level of human functionality in recognizing the surrounding world and predicting the future. The article analyzes foreign and domestic experience in the use of AI technologies in various areas of human life, as well as in marketing and merchandising in retail, and proposes an approach to improve the audit of the location of goods on the shelf, taking into account the replacement of intellectual human labor with artificial intelligence and the exclusion of human factors, checking the realogram and planogram using neurotechnologies. The use of artificial intelligence in reengineering is considered, an algorithm for auditing micro-merchandising as a business process is described, a problematic interview with grocery retail merchandisers is conducted, and the hypothesis about the possibility of automating a business process is confirmed. Software (Neisla application) was developed to automate the business process, its application was tested in a large regional retail chain of the Irkutsk region in Irkutsk. As a product category, "chocolate bars" are used. As a result of testing within one month, an increase in the economic efficiency of micro-merchandising as a business process was confirmed.