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This study concerns the sampled-data synchronization problem for spatiotemporal complex dynamical networks with switching topological structure, and the topologies switching is modeled by a semi-Markov process, where the transition rates between different modes are related to the sojourn-time. By constructing a suitable Lyapunov–Krasovskii functional including an improved looped-functional and utilizing the Bessel–Legendre inequality, two sufficient synchronization conditions with less conservatism are established. A sample-data controller which can ensure the error system stochastically stable with a prescribed dissipative performance is developed. Finally, three examples, which including normal numerical examples, comparative examples and an application example, are presented to demonstrate the validity and superiority of the developed methods.

Methane emissions from enteric fermentation in dairy cows make a substantial contribution to the greenhouse gas problem. Sniffer approaches represent a popular low-cost, mobile technology for cow gas emission measurement in industrial installations owing to their providing reliable emission estimates. Despite its advantages, the sniffer approach often yields data inconsistencies during automated acquisition due to unsynchronized clocks between sniffers and associated automatic milking machines, leading to uncertainty in the linking of each animal's data with methane emission records. Given the constantly growing demand for large-scale methane emission measurements for genetic studies, sniffer installations are expected to increase, making the need for an efficient solution to the data synchronization issue prescient. A novel approach for handling the synchronization problem was developed in this study based on matched filter theory. The approach was verified on gas emission data from multiple commercial dairy farms in Denmark. The results were analyzed and discussed in terms of accuracy and general characteristics of computational performance irrespective of a specific software implementation. The present findings support the conclusion that the presented matched filter-based approach is robust, applicable to the problem of cattle gas emission data synchronization, and convenient for automated data processing.

We developed a wearable experimental sensor setup featuring multimodal EEG+fNIRS neuroimaging data capture applicable for in situ experiments at a lower financial threshold. Consistent application of a good protocol and procedure for sensor application and signal quality control is crucial for researchers to obtain valid data. This paper provides an exhaustive description of the sensor setup, the data synchronization process, procedure for sensor application, and signal quality control. Potential design cognition experiments with the proposed EEG+fNIRS are also described. In summary, the setup is mobile and provides multimodal neuroimaging data of high quality. We encourage the design community to take advantage of the setup and adapt it to new experimental setups in situ.

This paper proposes a new approach to investigate the problem of designing sampled-data controller for master–slave synchronization of chaotic Lur’e systems with time delay. Compared with existing methods, this method can make full use of the information of actual sampling pattern. To reflect more realistic the information on both the intervals e(t) to $$e(t_{k})$$ and e(t) to $$e(t_{k+1})$$ , a novel two-side sampling-interval-dependent discontinuous Lyapunov functional (DLF) is constructed, which can fully utilizes the available characteristics of actual sampling information. Based on this DLF and by using modified free-matrix-based integral inequality, novel less conservative stability criteria of the synchronization error system are derived in the form of linear matrix inequalities, which guarantee the master system synchronizes with the slave system. At the same time, the gain matrix of the sampling controller is gained with a bigger sampling interval than the previous conclusions. Simulation results are provided to demonstrate the effectiveness and benefits of the presented synchronization scheme.

This brief is concerned with the drive-response synchronization problem of memristor networks subject to actuator failures and two different activations via sampled-data (SD) control strategy. By using $\delta $ -matrix measure method and Halanay inequality technique, the sufficient conditions for global exponential synchronization of driving-response memristor systems are obtained. Unlike the existing works, the common Lyapunov functionals are not employed, but two simple and general stability criteria are derived. Particularly, the established criteria are easier to demonstrate and execute in practice than the relevant results. And in order to verify the validity and superiority of the proposed theoretical results, a numerical simulation is finally provided.

This article deals with the issue of dissipative synchronization for Markov jump neural networks subject to time-varying interval delays by using the sampled-data control method. First, a new two-sided looped-functional is introduced to improve the informativeness of the formed Lyapunov–Krasovskii functional, which takes into an account the information of the entire sampling period. Compared with some traditional methods, the information between $x(t) $ and $x(t_{k+1})$ is also emphasized. On this basis, an improved inequality technique is considered valid to acquire the less conservative synchronization criterion. In the end, two numerical examples are displayed, and a comparative example powerfully demonstrates the availability and the superiority of the developed technique.

A multisource and heterogeneous database is an important problem that disturbs the use of the electric power information system. The existing database synchronization scheme has some problems in practical applications, such as high resource loss and poor portability. This paper presents a high-efficiency database synchronization scheme for the electric power information system. The database is monitored, and its changes are captured by the shadow table and trigger method. Thus, data could be exchanged in trusted networks and nontrusted networks. In addition, a predetermined strategy is used to avoid data conflicts and ensure consistency and reliability of data synchronization. The above method is applied in the protection system of power networks. The results show that the synchronization scheme can effectively ensure the security of the system and has higher synchronization efficiency.

Indonesia as an archipelago country has many residents whose livelihoods is fishermen. Traditional fishermen who are scattered in various provinces need a tool that can help to record fish catch data after they sail. This study aims to propose a method for synchronizing fish catch data between e-logbook and a fishing vessel monitoring system. The Unix timestamp reference was used to help synchronize fish catch data with the position in the monitoring system, and it was expected to run well. The results of this study are fisheries e-logbooks for Android devices equipped with data synchronization features. In the future, it would be better if a tool was made to help transmit data from the middle of the sea for traditional fishermen.

By facilitating multiple independent owners to jointly control a distributed network, blockchain can be used to solve the problem of device collaboration in complex networks (e.g., 5G, health care industries) through a distributed consensus mechanism. However, the state-of-the-art blockchain-based solutions cannot meet the demand of high transaction rate for those applications, due to the unavoidable data synchronization cost in decentralized systems. To address this issue, recent research splits blockchain nodes into multiple groups as parallel shardings to improve scalability at the cost of increased communication and storage per node. This paper proposes a fast and secure distributed blockchain protocol to reduce the traffic complexity while enhancing the transaction rates and the capability of fault-toleration. We introduce Proof-of-Behavior (PoB), a behavior-based incentive mechanism, for stimulating honest behavior and neutralizing malicious attacks. We design a blockchain protocol by integrating PoB with Raft, another classic consensus protocol with supervision, called Beh-Raft-Chain. Our approach replaces Practical Byzantine Fault Tolerance (PBFT) with Behavior-based Raft to lower the traffic complexity to O( n ) and boost the capability of fault-toleration from n /4 to n /3, where n is the scale of blockchain. In our solution, we weigh all nodes based on their money and behaviors, and then set an adjustment parameter to increase the probability of candidate nodes being chosen beyond only a few nodes with the highest weight, in order to incentivize honest behavior in our mechanism. Our comparative experiments confirm Beh-Raft-Chain's theoretical low complexity and high fault-toleration properties.

Complex deep-space missions containing several tasks are beyond the capacity of a single probe and require multi-probe cooperation. Such missions need onboard planning to avoid communication delays caused by the long distance between the probes and the Earth. However, the traditional centralized planning approaches will burden the manager probe with a high computing load and make the system liable to a single-point failure. A decentralized onboard planning system based on privacy-preserving multi-agent negotiation is proposed to address this problem. The Privacy-preserving Decentralized Multi-Agent Mission Planning Frame (PDMA-MPF) is designed, in which the concepts of private agent and public agent are proposed for the first time. Based on the planning frame, the Decentralized Multi-Agent Iterative Negotiation Task Allocation Method (DMAIN) is developed to allocate tasks and generate local plans for probes. The method uses a deferred-acceptance negotiation pattern, considering the mission profit and load balancing of probes. An information consistency mechanism is designed for the public data synchronization of planners. The local planning of probes is modeled as a constraint satisfaction problem with profit optimization. A temporal constraint handling algorithm and a Resource Rate Timeline (RRTL) based resource constraint handling algorithm are proposed to satisfy constraints in a profit optimized way. The planning system verification shows that the proposed system can generate reasonable plans and guarantee public data consistency between planners. The simulation results illustrate that the proposed planning system gets higher mission profits and a more balanced workload of probes, as compared with the traditional Hierarchical Greedy Algorithm.

The change of ownership and control of agricultural and non-agricultural land for all Indonesian people is a mandate of Article 7, 10 and 17 of Act Number 5 Year 1960 under the Indonesian Agrarian Reform. In practice, however, people can own property rights beyond the stipulated limit. The article aims to improve a fair distribution of land through the proposed model of supervision and property rights land tenure reforms. The data synchronization developed through an online system can be the tool to improve the supervision and management of land ownership and tenures. The methods used are the statute approach, socio-legal approach, and case study approach. The statute approach analysed existing statutes regarding land and land rights in Indonesia, the result is further observed in practice through the socio-legal approach by observing the data and figures in local regions. The case study approach reviews past judgments in the matter to examine the consistency and sufficiency of prevailing laws and policy and the direction of its developments. This study found that there is still an ineffective implementation of the law resulting in people having lands more than their limit. The proposed data synchronization model developed through an online system can solve this problem by harmonizing data in local regions with the existing data at the Civil Registry Office and the Tax Office. This study provides an essential contribution to the existing literature of Indonesian Agrarian Reform as well as a guideline for policymakers.

Model-based systems engineering (MBSE) plays a vital role in complex product development. In order to make the system modeling processes consistent with the business processes of the industry, it is necessary to use the product lifecycle management (PLM) system to manage system models. In this paper a SysML-based domain specific meta-model is proposed to support a visualization framework that includes graphics and data synchronization, which enables PLM software to present the product manufacturing data and models under the practicing of MBSE. Product manufacturing process and data are described, and the allocation of resource is represented in the early design phase.

On-device DNN processing has been common interests in the field of autonomous driving research. For better accuracy, both the number of DNN models and the model-complexity have been increased. To properly respond to this, hardware platforms structured with multicore-based CPUs and DNN accelerators have been released, and the GPU is generally used as an accelerator. When multiple DNN workloads are sporadically requested, the GPU can be easily oversubscribed, thereby leading to an unexpected performance bottleneck. We propose an on-device CPU-GPU co-scheduling framework for multi-DNN execution to remove the performance barrier precluding DNN executions from being bounded by the GPU. Our framework fills up the unused CPU cycles with DNN computations to ease the computational burden of the GPU. To provide seamless computing environment for the two different core types, the framework formats each layer execution according to the computational methods supported by CPU and GPU cores. To cope with irregular arrivals of DNN workloads, and to accommodate their fluctuating demands for hardware resources, our framework dynamically selects the best fit core type after making a comparative judgement between the current availabilities of the two core types. During the core selection time, offline-trained prediction models are utilized to get precisely predicted execution time of the issued layer. Our framework mitigates the fact that even the same DNN models can have large performance deviations due to the nature of the process scheduler of the underlying OS which is GPU-agnostic. In addition, the framework minimizes the memory copy overhead inevitably occurring in the data synchronization phase between the heterogeneous cores. To do so, we further analyze GPU-to-CPU and CPU-to-GPU data transfer cases separately, and then apply the solution that best suits each case. For multi-DNN inference jobs with the NVIDIA Jetson AGX Xavier platform, our framework speeds up the execution time by up to 46.6% over the GPU-only solution.

The sampled-data synchronization problem of chaotic neural networks with mixed time delays is investigated in this paper. A novel augmented discontinuous Lyapunov-Krasovskii functional (ADLKF) is constructed, which can make full capture the more information of delay variation and the characteristics about the actual sampling information. Based on the ADLKF and modified free-matrix-based integral inequality, novel less conservative synchronization criteria are developed to guarantee that the slave system is synchronous with the master system. Furthermore, the derived results are given in terms of simplified linear matrix inequalities (LMIs), which can be straightforwardly solved by Matlab. Finally, two numerical examples are proposed to demonstrate the effectiveness and the benefits of the presented synchronization scheme.

This paper proposes a new algorithm for the blockchain system based on multi-chain storage for CPS under edge cloud computing, in which the nodes of the system are divided according to the relationship of communication tightness.The experimental results show that the algorithm based on the multi-chain storage structure for nodes divided can reduce the data synchronization time and space, and improve the expansion ability of the system. Besides, the data in the system can be isolated from each other with the multi-chain structure, thus preventing the leakage of sensitive data and improving the information security of the system.

In a variety of network applications, there exists a significant amount of shared data between two end hosts. Examples include data synchronization services that replicate data from one node to another. Given that shared data may have a high correlation with new data to transmit, we question how such shared data can be best utilized to improve the efficiency of data transmission. To answer this, we develop an inter-data encoding technique, SyncCoding, that effectively replaces bit sequences of the data to be transmitted with the pointers to their matching bit sequences in the shared data so called references. By doing so, SyncCoding can reduce data traffic, speed up data transmission, and save energy consumption for transmission. Our evaluations of SyncCoding implemented in Linux show that it outperforms existing popular encoding techniques, Brotli, LZMA, Deflate, and Deduplication. The gains of SyncCoding over those techniques in the perspective of data size after compression in a cloud storage scenario are about 12.5, 20.8, 30.1, and 66.1 percent, and are about 78.4, 80.3, 84.3, and 94.3 percent in a web browsing scenario, respectively.

This paper deals with the synchronization control of general chaotic delayed neural networks via sampled-data controllers. The synchronization problem is reduced to a stabilization one, and then a Lyapunov–Krasovskii function (LKF) is proposed to obtain the sufficient condition for the asymptotic stability. Moreover, the sufficient condition is presented by linear matrix inequalities (LMIs), which are easy to solve by existing software. Three numerical examples including electrical circuits are provided to confirm validity of the theoretical results.

Among the different available technologies to estimate and forecast solar irradiance, total sky imagers are a suitable option for short-term horizons (less than 30 min). For this purpose, cameras are able to capture images at high speed (less than a second), depending on hardware and configuration, while solarimetric measurements are restricted to the characteristic response time of pyranometers, that varies from 15–60 s, depending on its quality. This difference in sampling time may lead to asynchronous data logging. As a result, two data sets with different sampling time are obtained. A close look into specialized literature showed a lack of evidence on data synchronization of reported results. Thus, the existence of asynchronous data is a plausible assumption. The main objective of this study is to investigate the effect of considering two different approaches in which data can be treated for the estimation of the solar irradiance and clearness index. For this purpose, two data sets are available: solar irradiance with a sampling time of 1 min, and sky images taken at 5 s intervals. Image acquisition system is based on a fisheye camera and a cost-effective and portable device. In one approach solar irradiance is interpolated to image time, and in the second approach, extracted image data is interpolated to solar irradiance time. For each case, solar irradiance and clearness index estimation is based on a multiple linear regression with two different number of features extracted from sky image processing. To evaluate these models, the mean bias error, root mean square error and the mean absolute percentage error were used. Obtained results shows that using different approaches to process data of measurements may lead to errors of 6.2% for the solar irradiance and 3% for the clearness index.

The work presented in this dissertation represents work which addresses some of the main challenges of fault localization methods in electrical distribution grids. The methods developed largely assume access to sophisticated data sources that may not be available and that any data sets recorded by devices are synchronized. These issues have created a barrier to the adoption of many solutions by industry. The goal of the research presented in this dissertation is to address these challenges through the development of three elements. These elements are a synchronization protocol, a fault localization technique, and a sensor placement algorithm. The synchronization protocol addresses the dependency on synchronized data by allowing the devices themselves to synchronize their data. This is accomplished by establishing relationships between temporal events, transient signals and the devices recording these events and signals. The protocol establishes a relationship between transient signals emitted from standard equipment in electrical grids and the fault event which is then leveraged to synchronize the recorded fault data. The method has shown very promising results in synchronizing data from multiple devices. The fault localization technique determines the location of a fault in a complex distribution grid. This is achieved by establishing spatial-temporal relationships between transient signals and distances in graphical representations of the distribution grid. This method provides precise fault locations in challenging electrical distribution grid structures. It also allows areas of the distribution grid to be classified based on how well they are monitored. This classification component predicts how well a distribution grid is monitored by the devices used. The sensor placement algorithm leverages the classification component established in the fault localization technique to optimize the placement of the fault localization devices in the distribution grid. Here, an efficient algorithm is created for determining the best location for a given number of fault localization devices. The combination of the synchronization protocol, a fault localization technique and a sensor placement algorithm and their minimal data requirements constitutes a uniquely complete solution that may set it apart from existing work and make it more attractive to the industry for adoption in electrical distribution grids.