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This work investigates the data quality issue for synchrophasor applications, and pays particular attention to synchronization signal loss and synchrophasor data loss events. First, the historical synchronization signal loss events are analyzed and the potential reasons and solutions are discussed. Then, the scenario of a small amount of synchrophasor data loss is studied and a Lagrange interpolating polynomial method is used to adaptively estimate the incomplete and missing data. The performance of proposed method is demonstrated with simulation results. Specifically, the proposed method considers the trade-off between the estimation accuracy and the hardware cost, and could be efficiently employed in reality.

Wireless networks are used extensively in communication technologies. From Tesla to date researchers, many are working on wireless data transmission techniques and technologies. Despite the immense progressions over the past decade, there are precise hurdles that the industry endures to face. Bandwidth restrictions, latency problems, and device compatibility issues prevent the viewer from meeting seamless data transmissions. Many devices used for communication are used with the wireless interface and are proficient in transmitting data efficiently to the communication range. The growth in data communication requirements increases the network traffic and results in a network bottleneck. There are more challenges for Mobile Ad hoc Networks (MANETs) due to the additional overhead of resource constraints. Congestion leads to depletion of the node's energy, deterioration of network performance, and increased network latency and packet loss. As a result, energy-efficient and reliable state-of-the-art congestion control protocols must be designed to detect, notify and control congestion effectively. To minimize the packet loss in MANETs using Transmission Control Protocol (TCP), we proposed a data loss minimization technique (DLMT). Results show that enhanced DLMT outperforms by 18% compared to state-of-the-art proven congestion control mechanisms. DLMT improves the Quality of Service (QoS) constraints and improves performance by reducing the delay in better throughput, which can be seen by analyzing experimental results. The proposed coordination process's scalability and robustness are shown in good agreement with simulation results and analytic results for the stochastic model.

This paper proposes a fast detection method for mixed bad data in power systems based on long short-term memory networks to address the problems of low detection accuracy and poor detection efficiency. This method utilizes the powerful processing capability and memory characteristics of LSTM networks for time series data, effectively addressing issues such as data loss, data corruption, synchronization anomalies, and noise impact in complex environments of power systems. By constructing a dual-layer LSTM network architecture, mixed bad data in the power system can be filtered out. By further standardizing the processing and improving the specific detection process, the rapid and effective detection of mixed bad data in the power system has been achieved. Simulation and actual data verification show that this method can significantly improve the data quality of the power system, enhance the accuracy and efficiency of detecting mixed bad data in the power system, and provide solid data support for the safe and stable operation of the power system.

The analysis of loss data is of utmost interest in many branches of the financial and insurance industries, in structural engineering and in operation research, among others. In the financial industry, the determination of the distribution of losses is the first step to take to compute regulatory risk capitals; in insurance we need the distribution of losses to determine the risk premia. In reliability analysis one needs to determine the distribution of accumulated damage or the first time of occurrence of a composite event, and so on. Not only that, but in some cases we have data on the aggregate risk, but we happen to be interested in determining the statistical nature of the different types of events that contribute to the aggregate loss. Even though in many of these branches of activity one may have good theoretical descriptions of the underlying processes, the nature of the problems is such that we must resort to numerical methods to actually compute the loss distributions. Besides being able to determine numerically the distribution of losses, we also need to assess the dependence of the distribution of losses and that of the quantities computed with it, on the empirical data. It is the purpose of this note to illustrate the how the maximum entropy method and its extensions can be used to deal with the various issues that come up in the computation of the distribution of losses. These methods prove to be robust and allow for extensions to the case when the data has measurement errors and/or is given up to an interval.

Individuals can perform many different behaviors to protect themselves from computer security threats. Research, however, generally explores computer security behaviors in isolation, typically looking at one behavior per study, such as usage of malware or strong passwords. However, defense in depth requires that multiple behaviors be performed concurrently for one’s computer to be protected. Addressing this gap in prior research, this study measures 279 individuals’ computer security behaviors and analyzes them with multi-dimensional scaling. We examined three security threats: security related performance degradation, identify theft, and data loss. The results present a mapping of security behaviors performed together with other behaviors on two dimensions for each of these threats. Using expert reviews of the resulting dimensions, the study proposes that response efficacy and response cost help explain why people perform certain behaviors together. These findings can help explain inconsistent results in prior information security research because they focused on one behavior only whereas people perform various security behaviors together in an effort to mitigate specific security threats. The study informs research and practice by identifying security threat-response pairs via expert interviews, surveying individuals on how they perform multiple security behaviors concurrently to mitigate security threats, identifying why certain behaviors are performed together, and using these findings to identify reasons why IS security research has confounding results based on specific individual threat-response pairs used in prior studies.

Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.

The article is devoted to the actual problem of information security of personal data, circulating on the internet portals of State government body and municipal government. Scientific development of technical aspects of the mechanism of electronic application of the public to the authority organs agrees with priority policy of creation of information society and electronic government, and personal data security is one of the tasks of integrated support of the state information security and this determines the relevance of the article topic. Basing on the analysis of legislation, content-analysis of portal centres of provision of state and municipal services, by carrying out experiments the problems have been found out and the proposals on technical information security of personal data while applying to the government body electronically have been developed. As a result of the content-analysis of regional portals of Multifunctional centres of provision of state and municipal services, it has been revealed that in most regional portals of Multifunctional centres personal data are sent in open, unencrypted form, which threatens their security. To study the possibility of interception of people personal data two experiments have been fulfilled. As a consequence of the experiment all the sent people's personal data have been got. On the basis of the received information the model of threats has been developed for information systems of personal data of Multifunctional centre, for this system values of initial system security, probability of threats realization have been defined, assessment of the possibility of threat realization and danger has been done. The solution to the problem of probable data loss while filling in electronic application can be obligatory usage of data encryption protocol HTTPS on the internet portals of public authority, this protocol is intended to provide the three most important security aspects: encryption, authentication (of users), integrity. To get authentication of users Multifunctional centres will have to give people their own digital signature, with the help of which applicant's personal data will be certified; data should be signed with the help of the privacy key of a Multifunctional centre. The results of the study are a software package aimed at secure data transfer, the layout of the site of a Multifunctional centre, recommended as a standard, proposals how to improve the legislation in the sphere of personal data security - will help to solve the problems of the society and state.

Aurora protocol is a high-speed serial communication protocol with characteristics such as high speed, low latency, open source and free. It is widely used in application fields that require a large amount of data transmission. However, Aurora communication systems also has the problem of data loss, making flow control a challenge; This paper analyzes the problem of data loss, finds out the root cause, and designs an NFC(Native Flow Control) system for the Aurora protocol. The system uses AXI4-Stream bus for communication and solves the problem by controlling the Aurora module to send NFC PAUSE code at the sending end. At the same time, by setting a reasonable upper limit value in the flow control program, the problem of data loss caused by NFC Latency is solved, and effective flow control is achieved.

In the era of healthcare, and its related research fields, the dimensionality problem of high dimensional data is a massive challenge as it contains a huge number of variables forming complex data matrices. The demand for dimension reduction of complex data is growing immensely to improvise data prediction, analysis and visualization. In general, dimension reduction techniques are defined as a compression of dataset from higher dimensional matrix to lower dimensional matrix. Several computational techniques have been implemented for data dimension reduction, which is further segregated into two categories such as feature extraction and feature selection. In this review, a detailed investigation of various feature extraction and feature selection methods has been carried out with a systematic comparison of several dimension reduction techniques for the analysis of high dimensional data and to overcome the problem of data loss. Then, some case studies are also cited to verify the better approach for data dimension reduction by considering few advances described in the technical literature. This review paper may guide researchers to choose the most effective method for satisfactory analysis of high dimensional data.

According to the development of deep learning technologies, a wide variety of research is being performed to detect intrusion data by using vast amounts of data. Although deep learning performs more accurately than machine learning algorithms when learning large amounts of data, the performance declines significantly in the case of learning from imbalanced data. And, while there are many studies on imbalanced data, most have weaknesses that can result in data loss or overfitting. The purpose of this study is to solve data imbalance by using the Generative Adversarial Networks (GAN) model, which is an unsupervised learning method of deep learning which generates new virtual data similar to the existing data. It also proposed a model that would be classified as Random Forest to identify detection performance after addressing data imbalances based on a GAN. The results of the experiment showed that the performance of the model proposed in this paper was better than the model classified without addressing the imbalance of data. In addition, it was found that the performance of the model proposed in this paper was excellent when compared with other models that were previously used widely for the data imbalance problem.