Multi-source Network Research Articles

Temperature level optimization for low-grade thermal networks using the exergy method

Low-temperature thermal networks open the field for additional renewable and recovered energy sources to be used. The exploitation of low exergy level resources requires decentralized heat pumps having a significant impact on the network's overall electricity consumption. Thus, a compromise must be found in order to minimize thermal and electrical consumption while integrating a maximum of renewable energy sources. This optimum is governed by the temperature level of the network. This paper aims at determining the optimal network temperature using the exergy criterion. The exergy method is detailed and applied to the multi-source network blueCAD (Fribourg) fed by geothermal energy, and FriCAD, a high temperature district heating network. The optimum temperature decreases as the share of geothermal energy in the production increases. For blueCAD, it ranges from 40 to 55 °C.

P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection.

Single-trial electroencephalogram detection has been widely applied in brain-computer interface (BCI) systems. Moreover, an individual generalized model is significant for applying the dynamic visual target detection BCI system in real life because of the time jitter of the detection latency, the dynamics and complexity of visual background. Hence, we developed an unsupervised multi-source domain adaptation network (P3-MSDA) for dynamic visual target detection. In this network, a P3 map-clustering method was proposed for source domain selection. The adversarial domain adaptation was conducted for domain alignment to eliminate individual differences, and prediction probabilities were ranked and returned to guide the input of target samples for imbalanced data classification. The results showed that individuals with a strong P3 map selected by the proposed P3 map-clustering method perform best on the source domain. Compared with existing schemes, the proposed P3-MSDA network achieved the highest classification accuracy and F1 score using five labeled individuals with a strong P3 map as the source domain. These findings can have a significant meaning in building an individual generalized model for dynamic visual target detection.

Certificateless Network Coding Ring Signature Scheme

Traditional certificateless ring signature is not suitable for the network coding environment. In this view, a new certificateless network coding ring signature (CL-NCRS) is devised by using the techniques of multisource network coding and certificateless ring signature. Network coding is a kind of information interaction technology and can improve system robustness and save wireless network resources. CL-NCRS satisfies the unforgeability and unconditional anonymity; moreover, it has relatively higher communication and computational efficiency.

Reconstruction of the miR-506-Quaking axis in Idiopathic Pulmonary Fibrosis using integrative multi-source bioinformatics

The family of RNA-binding proteins (RBP) functions as a crucial regulator of multiple biological processes and diseases. However, RBP function in the clinical setting of idiopathic pulmonary fibrosis (IPF) is still unknown. We developed a practical in silico screening approach for the characterization of RBPs using multi-sources data information and comparative molecular network bioinformatics followed by wet-lab validation studies. Data mining of bulk RNA-Sequencing data of tissues of patients with IPF identified Quaking (QKI) as a significant downregulated RBP. Cell-type specific expression was confirmed by single-cell RNA-Sequencing analysis of IPF patient data. We systematically analyzed the molecular interaction network around QKI and its functional interplay with microRNAs (miRs) in human lung fibroblasts and discovered a novel regulatory miR-506-QKI axis contributing to the pathogenesis of IPF. The in silico results were validated by in-house experiments applying model systems of miR and lung biology. This study supports an understanding of the intrinsic molecular mechanisms of IPF regulated by the miR-506-QKI axis. Initially applied to human lung disease, the herein presented integrative in silico data mining approach can be adapted to other disease entities, underlining its practical relevance in RBP research.

Computing Personality Trait Based on Multi-source

Personality computing is a hot research field recently, which mainly uses the individual’s traces on social platforms to understand, predict and analyze their behaviors, to make a certain accurate judgment of user personality types. The application of personality traits is of great significance to intelligent medical, personalized service customization, and other fields. The current personality computing research is mainly based on social media, the data source is limited, can not reflect the real situation. This paper collects college volunteers’ network access logs, the data is more comprehensive and complex, not limited to only the traces on social media. According to these network access logs, the calculation results show that the personality traits of users in different social media are not consistent, and the access preferences of network resources are not exactly the same as the personality traits reflected by social media. Therefore, it is more accurate to integrate multi-source network information to calculate an individual personality than a single data source.

Research on the Key Technologies of Network Security-Oriented Situation Prediction

In today’s increasingly severe network security situation, network security situational awareness provides a more comprehensive and feasible new idea for the inadequacy of various single solutions and is currently a research hotspot in the field of network security. At present, there are still gaps or room for improvement in network security situational awareness in terms of model scheme improvement, comprehensive and integrated consideration, algorithm design optimization, etc. A lot of scientific research investments and results are still needed to improve the form of network security in a long and solid way. In this paper, we propose a network security posture assessment model based on time-varying evidence theory for the existing multisource information fusion technology that lacks consideration of the problem of threat occurrence support rate over time and make the threat information reflect the law of time change by introducing a time parameter in the basic probability assignment value. Thus, the existing hierarchical threat posture quantitative assessment technique is improved and a hierarchical multisource network security threat posture assessment model based on time-varying evidence theory is proposed. Finally, the superiority of the proposed model is verified through experiments.

Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning.

Uncovering the complex network of the brain is of great interest to the field of neuroimaging. Mining from these rich datasets, scientists try to unveil the fundamental biological mechanisms in the human brain. However, neuroimaging data collected for constructing brain networks is generally costly, and thus extracting useful information from a limited sample size of brain networks is demanding. Currently, there are two common trends in neuroimaging data collection that could be exploited to gain more information: 1) multimodal data, and 2) longitudinal data. It has been shown that these two types of data provide complementary information. Nonetheless, it is challenging to learn brain network representations that can simultaneously capture network properties from multimodal as well as longitudinal datasets. Here we propose a general fusion framework for multi-source learning of brain networks - multimodal brain network fusion with longitudinal coupling (MMLC). In our framework, three layers of information are considered, including cross-sectional similarity, multimodal coupling, and longitudinal consistency. Specifically, we jointly factorize multimodal networks and construct a rotation-based constraint to couple network variance across time. We also adopt the consensus factorization as the group consistent pattern. Using two publicly available brain imaging datasets, we demonstrate that MMLC may better predict psychometric scores than some other state-of-the-art brain network representation learning algorithms. Additionally, the discovered significant brain regions are synergistic with previous literature. Our new approach may boost statistical power and sheds new light on neuroimaging network biomarkers for future psychometric prediction research by integrating longitudinal and multimodal neuroimaging data.

Performance evaluation of joint relay selection and network coding for multi-source and multi-destination relay network

Network coding and relay selection have attracted significant attention due to their performance improvement and spectral efficiency in wireless networks. In this work, we study joint relay selection and network coding cooperation schemes for M source-destination pairs assisted with N relays. We propose a new smart decode-and-forward max-min relay selection scheme based on the source-destination links’ outage status and L selected relays to enhance the communication system diversity. Our objective is realize joint network coding and relay selection strategies for maximizing the expected number of recovered packets by the intended destinations in various situations. We analyze the performance of the proposed scheme and prove that the maximum diversity order of $$N+1$$ is achieved when the number of active relays is at least $$\lceil {\frac{N+1}{3}}\rceil $$ . Also, we show that the scheme can achieve a diversity order of 3L when $$L<\lceil {\frac{N+1}{3}}\rceil $$ . Our analytical and simulation results indicate that the proposed scheme provides higher performance than the existing network coded cooperation schemes.

Design of Minimizing Expected Energy of Multisource Wireless Cooperative Network Based on Multiobjective Optimization

In order to solve the problems of high average power consumption, low average throughput, high average energy consumption per unit of data, and short network life cycle in traditional multisource wireless cooperation methods, this paper proposes a multisource wireless cooperative network design method based on multiple goals. We analyze the characteristics of heterogeneous deployment of multisource wireless cooperative networks and the energy consumption of nodes and control the energy consumption of network data transmission through distributed opportunistic transmission scheduling methods according to the analysis results. We use the optimal strategy of minimizing expected energy consumption, transform the problem of data transmission energy consumption, establish a mathematical model, and obtain the optimal solution for minimizing expected energy consumption. According to the optimal stop rule, the minimum expected energy consumption threshold is obtained, and then the optimal solution is obtained on the constraint set of the multiobjective optimization problem through the multiobjective optimization method, so as to achieve the goal of minimizing the expected energy of the network. Experimental results show that this method prolongs the network life cycle, reduces the average power consumption of network data transmission, and improves the average network throughput.

Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

Optimal Sampling and Scheduling for Timely Status Updates in Multi-Source Networks

We consider a joint sampling and scheduling problem for optimizing data freshness in multi-source systems. Data freshness is measured by a non-decreasing penalty function of age of information, where all sources have the same age-penalty function. Sources take turns to generate update packets, and forward them to their destinations one-by-one through a shared channel with random delay. There is a scheduler, that chooses the update order of the sources, and a sampler, that determines when a source should generate a new packet in its turn. We aim to find the optimal scheduler-sampler pairs that minimize the total-average age-penalty at delivery times (Ta-APD) and the total-average age-penalty (Ta-AP). We prove that the Maximum Age First (MAF) scheduler and the zero-wait sampler are jointly optimal for minimizing the Ta-APD. Meanwhile, the MAF scheduler and a relative value iteration with reduced complexity (RVI-RC) sampler are jointly optimal for minimizing the Ta-AP. The RVI-RC sampler is based on a relative value iteration algorithm whose complexity is reduced by exploiting a threshold property in the optimal sampler. Finally, a low-complexity threshold-type sampler is devised via an approximate analysis of Bellman's equation. This threshold-type sampler reduces to a simple water-filling sampler for a linear age-penalty function.

System Reliability Optimization of Multi-Source Multi-Sink Stochastic Flow Networks With Budget Constraint

In this paper, we investigate system reliability optimization of multi-source multi-sink flow networks subject to transmission budget constraints. More specifically, we present a mathematical model of the optimization problem and a genetic algorithm (GA) to solve it. The GA is based on determining the optimal set of lower boundary points that maximize system reliability such that transmission cost does not exceed a specified upper bound. Finally, to ensure the efficiency of our approach, we apply our proposed algorithm to various network examples.

A transfer learning model with multi-source domains for biomedical event trigger extraction

BackgroundAutomatic extraction of biomedical events from literature, that allows for faster update of the latest discoveries automatically, is a heated research topic now. Trigger word recognition is a critical step in the process of event extraction. Its performance directly influences the results of the event extraction. In general, machine learning-based trigger recognition approaches such as neural networks must to be trained on a dataset with plentiful annotations to achieve high performances. However, the problem of the datasets in wide coverage event domains is that their annotations are insufficient and imbalance. One of the methods widely used to deal with this problem is transfer learning. In this work, we aim to extend the transfer learning to utilize multiple source domains. Multiple source domain datasets can be jointly trained to help achieve a higher recognition performance on a target domain with wide coverage events.ResultsBased on the study of previous work, we propose an improved multi-source domain neural network transfer learning architecture and a training approach for biomedical trigger detection task, which can share knowledge between the multi-source and target domains more comprehensively. We extend the ability of traditional adversarial networks to extract common features between source and target domains, when there is more than one dataset in the source domains. Multiple feature extraction channels to simultaneously capture global and local common features are designed. Moreover, under the constraint of an extra classifier, the multiple local common feature sub-channels can extract and transfer more diverse common features from the related multi-source domains effectively. In the experiments, MLEE corpus is used to train and test the proposed model to recognize the wide coverage triggers as a target dataset. Other four corpora with the varying degrees of relevance with MLEE from different domains are used as source datasets, respectively. Our proposed approach achieves recognition improvement compared with traditional adversarial networks. Moreover, its performance is competitive compared with the results of other leading systems on the same MLEE corpus.ConclusionsThe proposed Multi-Source Transfer Learning-based Trigger Recognizer (MSTLTR) can further improve the performance compared with the traditional method, when the source domains are more than one. The most essential improvement is that our approach represents common features in two aspects: the global common features and the local common features. Hence, these more sharable features improve the performance and generalization of the model on the target domain effectively.

A certificateless signature for multi-source network coding

Comparison with traditional network routing technology, multi-source network coding allows the routers to encode the received data and has the merits of large throughput, strong robustness and fast speed. In addition, certificateless public key cryptosystem (CL-PKC) is both certificate-free and key escrow-free. From now, there is no certificateless signature suitable for multi-source network coding (MSNC-CLS). In view of its wide application in practice, we construct an MSNC-CLS by applying the technique of certificateless signature to the environments of multi-source network coding. In MSNC-CLS, the use of generation can defend the replay attacks and the homomorphism of hash function can simplify the verification process of intermediate nodes. Generation identifier can make the intermediate nodes judge the generation property of message, thus the intermediate nodes can decide whether they encode the message. Analysis shows MSNC-CLS can resist the pollution and forgery attacks; moreover, it has better computation performance than the existing schemes.

Cooperative Physical Layer Security Transmission Scheme Based on Energy Harvesting

Energy harvesting (EH) is introduced into the physical layer security transmission of wireless sensor networks. It takes the energy efficiency and security into account simultaneously. To improve the physical layer security of the cooperative wireless sensor network, an EH-based physical layer security transmission scheme called multi-relay time switching (MRTS) is proposed in this paper. In the multi-source single-destination network with eavesdroppers, multiple relay nodes with time switching (TS) protocol convert the received signals into energy and information according to the time slots, and the converted energy is used to amplify and forward the received information. The research shows the optimal time switching ratio, under which the maximum secrecy capacity is achievable with the optimal converted energy. The results prove that under the same conditions, the secure transmission scheme based on MRTS is better than power splitting-based relaying (PSR) scheme when the total power of sources is limited.

Studi Pemanfaatan Relai Jarak Quadrilateral untuk Proteksi Saluran Distribusi dengan Pembangkit Tersebar

Installation of distributed generations (DGs) in distribution network bring benefits for power system operation. However, DGs change the distribution network topologies from radial with one source into multi-sources network, hence magnitude and direction of the current will also change. Consequently, the existing distribution line protection i.e. current based protection, experience performances deterioration. To overcome this problem, distance relays, which work based on calculated impedance is proposed. However, since mho distance relays fail to detect faults through resistance in previous research, quadrilateral characteristic of distance relay is chosen. Quadrilateral distance relays have separate setting for reach and resistance; hence it is expected it will accommodate fault resistance. Therefore, this research aims to identify the performance of quadrilateral distance relay in protecting distribution lines that having DG. The research method is computer simulation where quadrilateral distance relays performance is tested for various faults and system conditions in a test system. The software for the simulation is Digsilent Power factory. The results conclude that the quadrilateral distance relays experienced failures to detect faults in correct zone operation due to a weak source of DG. DG generates less fault current, as consequences, the relays see large impedances.

Power management of multi-source network hydraulic system with multiple actuators

The mismatch between the load and pump sources and the restriction of energy recovery lead to large energy losses and lower energy efficiency in high power multi-actuator hydraulic systems. To obtain high energy efficiency, this paper develops a Multi-Source Network Hydraulic System (MSNHS) with multiple actuators and a corresponding power management strategy (PMS) to reduce the throttling losses and recover energy. The multiple energy-saving power modes based on the path of power transmissions, composed of different combinations of six power units with energy conversion and transfer, are implemented on MSNHS to supply and regenerate or recover power for various load conditions. Thus, the power consumption models of each power unit are established to analyze the power consumption characteristics for itself and power modes. On this basis, the switching supervisory controller is proposed to select an optimal mode for the current condition by utilizing an objective optimized function of minimal power consumption. Besides, the switching rules among power modes are designed, and the logical threshold control is presented for the multi-mode switching under different working conditions. The combination of the switching supervisory controller of optimal mode and the logical threshold control realizes the power management of the MSNHS with multiple actuators to accommodate with time-varying load and recover potential energy. The experimental validation is conducted on the MSNHS platform by simulating a 90° truck-loading cycle of the excavator. Experimental results demonstrate that the PMS applied to the MSNHS can downsize engine input power by up to 60.27% compared with the theoretical calculation of conventional one, and then improve overall system energy efficiency.

Nonsignaling causal hierarchy of general multisource networks

Large-scale multisource networks have been employed to overcome the practical constraints that entangled systems are difficult to faithfully transmit over large distance or store in long time. However, a full characterization of the multipartite nonlocality of these networks remains out of reach, mainly due to the complexity of multipartite causal models. In this paper, we propose a general framework of Bayesian networks to reveal connections among different causal structures. The present model implies a special star-convex set of non-signaling correlations from multisource networks that allows constructing polynomial-time algorithm for solving the compatibility problem of a given correlation distribution and a fixed causal network. It is then used to classify the nonlocality originated from the standard entanglement swapping of tripartite networks. Our model provides a unified device-independent information processing method for exploring the practical security against non-signaling eavesdroppers on multisource quantum networks.

Dynamic flows with adaptive route choice

We study dynamic network flows and introduce a notion of instantaneous dynamic equilibrium (IDE) requiring that for any positive inflow into an edge, this edge must lie on a currently shortest path towards the respective sink. We measure current shortest path length by current waiting times in queues plus physical travel times. As our main results, we show: existence and constructive computation of IDE flows for multi-source single-sink networks assuming constant network inflow rates,finite termination of IDE flows for multi-source single-sink networks assuming bounded and finitely lasting inflow rates,the existence of IDE flows for multi-source multi-sink instances assuming general measurable network inflow rates,the existence of a complex single-source multi-sink instance in which any IDE flow is caught in cycles and flow remains forever in the network.

An Analytical Game for Knowledge Acquisition for Maritime Behavioral Analysis Systems

The use of Bayesian networks for behavioral analysis is gaining attention. The design of such algorithms often makes use of expert knowledge. The knowledge is collected and organized during the knowledge acquisition design task. In this paper, we discuss how analytical games can be exploited as knowledge acquisition techniques in order to collect information useful to intelligent systems design. More specifically, we introduce a recently developed method, called the MARISA (MARItime Surveillance knowledge Acquisition) Game. The aim of this game is to ease the elicitation from domain experts of a considerable amount of conditional probabilities to be encoded into a maritime behavioral analysis service based on a multi-source dynamic Bayesian network. The game has been deployed in two experiments. The main objectives of such experiments are the validation of the network structure, the acquisition of the conditional probabilities for the network, and the overall validation of the game method. The results of the experiment show that the objectives have been met and that the MARISA Game proved to be an effective and efficient approach.