Biblio

In recent years, with the occurrence of climate change and various extreme events, the research on the resistance of physical information systems to large-scale complex faults is of great significance. Propose a power information system to deal with complex faults in extreme weather, establish an anti-interference framework, construct a regional anti-interference strategy based on regional load output matching and topological connectivity, and propose branch active power adjustment methods to reduce disasters. In order to resist the risk of system instability caused by overrun of branch power and phase disconnection, the improved IEEE33 node test system simulation shows that this strategy can effectively reduce the harm of large-scale and complex faults.

Historically, energy resources are of strategic importance for the social welfare and economic growth. So, predicting crude oil price fluctuations is an important issue. Since crude oil price changes are affected by many risk factors in markets, this price shows more complicated nonlinear behavior and creates more risk levels for investors than in the past. We propose a new method of prediction of crude oil price to model nonlinear dynamics. The results of the experiments show that the superior performance of the model based on the proposed method against statistical previous works is statistically significant. In general, we found that the combination of the IDBN or LSTM model lowered the MSE value to 4.65, which is 0.81 lower than the related work (Chen et al. protocol), indicating an improvement in prediction accuracy.

With the rapid development of cloud computing, IaaS (Infrastructure as a Service) becomes more and more popular. IaaS customers may not clearly know the actual performance of each cloud platform. Moreover, there are no unified standards in performance evaluation of IaaS VMs (virtual machine). The underlying virtualization technology of IaaS cloud is transparent to customers. In this paper, we will design an automated performance benchmarking platform which can automatically install, configure and execute each benchmarking tool with a configuration center. This platform can easily visualize multidimensional benchmarking parameters data of each IaaS cloud platform. We also rented four IaaS VMs from AliCloud-Beijing, AliCloud-Qingdao, UCloud and Huawei to validate our benchmarking system. Performance comparisons of multiple parameters between multiple platforms were shown in this paper. However, in practice, customers' applications running on VMs are often complex. Performance of complex applications may not depend on single benchmarking parameter (e.g. CPU, memory, disk I/O etc.). We ran a TPC-C test for example to get overall performance in MySQL application scenario. The effects of different benchmarking parameters differ in this specific scenario.

The spatial-temporal random variation characteristics of underwater acoustic channel make the difference among the underwater acoustic communication network link channels, which make network performance difficult to predict. In order to better understand the fluctuation and difference of network link channel, we analyze the measured channel data of five links in the Qiandao Lake underwater acoustic communication network experiment. This paper first estimates impulse response, spread function, power delay profile and Doppler power spectrum of the time-varying channel in a short detection time, and compares the time-frequency energy distribution characteristics of each link channel. Then, we statistically analyze the discreteness of the signal to noise ratio, multipath spread and Doppler spread parameter distributions for a total of145 channels over a long observation period. The results show that energy distribution structure and fading fluctuation scale of each link channel in underwater acoustic communication network are obviously different.

As a typical cyber-physical system, the power system utilizes advanced information and communication technologies to transmit crucial control signals in communication channels. However, many adversaries can construct false data injection attacks (FDIA) to circumvent traditional bad data detection and break the stability of the power grid. In this paper, we proposed a threat-maximizing FDIA model from the view of attackers. The proposed FDIA can not only circumvent bad data detection but can also cause a terrible fluctuation in the power system. Furthermore, in order to eliminate potential attack threats, the Spatio-temporal correlations of measurement matrices are considered. To extract the Spatio-temporal features, a data-driven detection method using a deep convolutional neural network was proposed. The effectiveness of the proposed FDIA model and detection are assessed by a simulation on the New England 39 bus system. The results show that the FDIA can cause a negative effect on the power system’s stable operation. Besides, the results reveal that the proposed FDIA detection method has an outstanding performance on Spatio-temporal features extraction and FDIA recognition.

Trust in autonomous teammates has been shown to be a key factor in human-autonomy team (HAT) performance, and anthropomorphism is a closely related construct that is underexplored in HAT literature. This study investigates whether perceived anthropomorphism can be measured from team communication behaviors in a simulated remotely piloted aircraft system task environment, in which two humans in unique roles were asked to team with a synthetic (i.e., autonomous) pilot agent. We compared verbal and self-reported measures of anthropomorphism with team error handling performance and trust in the synthetic pilot. Results for this study show that trends in verbal anthropomorphism follow the same patterns expected from self-reported measures of anthropomorphism, with respect to fluctuations in trust resulting from autonomy failures.

The security of Internet of Things (IoT) devices has become a matter of great concern in recent years. The existence of security holes in the executable programs in the IoT devices has resulted in difficult to estimate security risks. For a long time, vulnerability detection is mainly completed by manual debugging and analysis, and the detection efficiency is low and the accuracy is difficult to guarantee. In this paper, the mainstream automated vulnerability analysis methods in recent years are studied, and a vulnerability detection algorithm based on symbol execution is presented. The detection algorithm is suitable for lightweight applications in small and medium-sized IoT devices. It realizes three functions: buffer overflow vulnerability detection, encryption reliability detection and protection state detection. The robustness of the detection algorithm was tested in the experiment, and the detection of overflow vulnerability program was completed within 2.75 seconds, and the detection of encryption reliability was completed within 1.79 seconds. Repeating the test with multiple sets of data showed a small difference of less than 6.4 milliseconds. The results show that the symbol execution detection algorithm presented in this paper has high detection efficiency and more robust accuracy and robustness.

As the extensive use of the wireless sensor networks in Advanced Metering Infrastructure (AMI) of Smart Grid, the network security of AMI becomes more important. Thus, an optimization of trust management mechanism of Beta distribution theory is put forward in this article. First of all, a self-adaption method of trust features sampling is proposed, that adjusts acquisition frequency according to fluctuation of trust attribute collected, which makes the consumption of network resource minimum under the precondition of ensuring accuracy of trust value; Then, the collected trust attribute is judged based on the Mahalanobis distance; Finally, calculate the nodes’ trust value by the optimization of the Beta distribution theory. As the simulation shows, the trust management scheme proposed is suited to WSNs in AMI, and able to reflect the trust value of nodes in a variety of circumstances change better.

Batched network coding is a low-complexity network coding solution to feedbackless multi-hop wireless packet network transmission with packet loss. The data to be transmitted is encoded into batches where each of which consists of a few coded packets. Unlike the traditional forwarding strategy, the intermediate network nodes have to perform recoding, which generates recoded packets by network coding operations restricted within the same batch. Adaptive recoding is a technique to adapt the fluctuation of packet loss by optimizing the number of recoded packets per batch to enhance the throughput. The input rank distribution, which is a piece of information regarding the batches arriving at the node, is required to apply adaptive recoding. However, this distribution is not known in advance in practice as the incoming link's channel condition may change from time to time. On the other hand, to fully utilize the potential of adaptive recoding, we need to have a good estimation of this distribution. In other words, we need to guess this distribution from a few samples so that we can apply adaptive recoding as soon as possible. In this paper, we propose a distributionally robust optimization for adaptive recoding with a small-sample inferred prediction of the input rank distribution. We develop an algorithm to efficiently solve this optimization with the support of theoretical guarantees that our optimization's performance would constitute as a confidence lower bound of the optimal throughput with high probability.

The continuous integration of intermittent low-carbon energy resources makes islanded microgrids vulnerable to voltage fluctuations. Besides, different dynamic response of synchronous-based and inverter-based distributed generation (DG) units can result in an instantaneous power imbalance between supply and demand during transients. As a result, the ac-bus voltage of microgrid starts oscillating which might have severe consequences such as blackouts. This paper modifies the conventional control scheme of battery energy storage systems (BESSs) to participate in improving the dynamic behavior of islanded microgrids by mitigating the voltage fluctuations. A piecewise linear-elliptic (PLE) droop is proposed and employed in BESS to achieve an enhanced voltage profile by injecting/absorbing reactive power during transients. In this way, the conventional inverter implemented in BESS turns into a smart inverter to cope with fast transients. Using the proposed approach in this paper, any linear droop curve with a specified coefficient can be replaced by a PLE droop curve. Compared with linear droop, an enhanced dynamic response is achieved by utilizing the proposed PLE droop. Case study results are presented using PSCAD/EMTDC to demonstrate the superiority of the proposed approach in improving the dynamic behavior of islanded microgrids.

The vulnerability of islanded microgrids to voltage and frequency variations is due to the presence of low-inertia distributed generation (DG) units. Besides, the considerable difference between the inertia of synchronous-based and inverter-based DGs results in a power mismatch between generation and consumption during abnormal conditions. As a result, both voltage and frequency of microgrid ac-bus start oscillating which might lead to blackouts. This paper deploys the traditional controller of photovoltaic (PV) units to improve the dynamics of islanded microgrids by reducing the voltage and frequency deviations. To this end, an adaptive piecewise droop (APD) curve is presented and implemented in PV units to attain a faster balance between supply and demand during transients, leading to an enhanced frequency response. Besides, the reactive-power control loop is equipped with a droop characteristic which enables the PV units to inject/absorb reactive power during transients and participate in voltage-profile enhancement of the system. Case study results are presented using PSCAD/EMTDC to confirm the validity of proposed method in improving the dynamic behavior of islanded microgrids.

In recent years, biometric authentication, such as fingerprint and face recognition, has become widespread in smartphones. However, fingerprint and face authentication have the problem that they cannot be used depending on the condition of the user's fingers or face. Therefore, we have been investigating a new biometric authentication system using pinna as a personal authentication system for smart phones. We have studied a personal authentication system using the Pinna Related Transfer Function (PRTF), which is an acoustic transfer function measured from the pinna. However, since the position of the smartphone changes every time it is placed on the ear, there is a problem that the authentication rate decreases. In this paper, we propose a multimodal personal authentication system using PRTF, pinna images, and smartphone location information, and verify its effectiveness. The results show that the proposed authentication system can improve the robustness against the fluctuation of the smartphone location.

Acoustic tomography experiments for ocean observation require low-frequency, broadband, high power, small size underwater acoustic transducer, but there are contradictions between the performance of the transducer, therefore a longitudinal-bending fluid-cavity coupled broadband underwater acoustic transducer is presented. The difference between the transducer and the traditional JH transducer is that the opening position of the Helmholtz resonant cavity is arranged between the radiation cover plate and the cylindrical cavity. Based on the optimization results of the finite element software ANSYS produced a transducer test prototype. The test results show that the simulation results and experimental results are basically consistent, and the transmitting voltage response can reach 136dB, the transmitting voltage response fluctuation shall no more than 6dB through the range of 700-1200Hz in the horizontal direction, verified the longitudinal-bending mode and the fluid-cavity mode of the transducer are well coupled, and the transducer is an ideal low-frequency, broadband, high power, small size underwater acoustic transducer.

Most financial markets prohibit unfair trades as they reduce efficiency and diminish the integrity of the market. Spoofers place orders they have no intention of trading in order to manipulate market prices and profit illegally. Most financial markets prohibit such spoofing orders; however, further clarification is still needed regarding how many orders a spoofer needs to place in order to manipulate market prices and profit. In this study I built an artificial market model (an agent-based model for financial markets) to show how unbalanced buy and sell orders affect the expected returns, and I implemented the spoofer agent in the model. I then investigated how many orders the spoofer needs to place in order to manipulate market prices and profit illegally. The results indicate that showing more spoofing orders than waiting orders in the order book enables the spoofer to earn illegally, amplifies price fluctuation, and reduces the efficiency of the market.

In order to solve the statistical fluctuation problem caused by the finite data length in the practical quantum key distribution system, four commonly used statistical methods, DeMoivre-Laplace theorem, Chebyshev inequality, Chernoff boundary and Hoeffding boundary, are used to analyze. The application conditions of each method are discussed, and the effects of data length and confidence level on quantum key distribution security performance are simulated and analyzed. The simulation results show that the applicable conditions of Chernoff boundary are most consistent with the reality of the practical quantum key distribution system with finite-length data. Under the same experimental conditions, the secure key generation rate and secure transmission distance obtained by Chernoff boundary are better than those of the other three methods. When the data length and confidence level change, the stability of the security performance obtained by the Chernoff boundary is the best.

Dynamic properties of optical signals in fiber channel provide a unique, random and reciprocal source for physical-layer secure key generation and distribution (SKGD). In this paper, an inherent physical-layer SKGD scheme is proposed and demonstrated, where the random source is originated from the dynamic fluctuation of the instant state of polarization (SOP) of optical signals in fiber. Due to the channel reciprocity, highly-correlated fluctuation of Stokes parameter of SOP is shared between the legal partners, where an error-free key generation rate (KGR) of 196-bit/s is successfully demonstrated over 25-km standard single-mode fiber (SSMF). In addition, an active polarization scrambler is deployed in fiber to increase the KGR, where an error-free KGR of 200-kbit/s is achieved.

We re-use a polarization-multiplexed I/Q modulator to acquire the quantum randomness of its seed laser light for the purpose of quantum random number generation. We obtain 9×104 256-bit AES keys/second after randomness extraction. Time-interleaved random number generation is demonstrated for PM-QPSK transmission.

This paper attempts to clarify the concept and applications of Li-Fi technology. The current Wi-Fi network use Radio Frequency waves, but the usage of the available RF spectrum is limited. Therefore a new technology, Li-Fi has come into picture. Li-Fi is a recently developed technology. This paper explains how array of LEDs are used to transmit data in the visible light spectrum. This technology has advantages like security, increased accessible spectrum, low latency efficiency and much higher speed as compared to Wi- Fi. The aim of this research paper is to design a Li-Fi transceiver using Arduino which is able to transmit and receive data in binary format. The software coding is done in Arduino- Uno platform. Successful transmission and reception of data(alphanumeric) has been done.

NDN has been widely regarded as a promising representation and implementation of information- centric networking (ICN) and serves as a potential candidate for the future Internet architecture. However, the security of NDN is threatened by a significant safety hazard known as an IFA, which is an evolution of DoS and distributed DoS attacks on IP-based networks. The IFA attackers can create numerous malicious interest packets into a named data network to quickly exhaust the bandwidth of communication channels and cache capacity of NDN routers, thereby seriously affecting the routers' ability to receive and forward packets for normal users. Accurate detection of the IFAs is the most critical issue in the design of a countermeasure. To the best of our knowledge, the existing IFA countermeasures still have limitations in terms of detection accuracy, especially for rapidly volatile attacks. This article proposes a TC to detect the distributions of normal and malicious interest packets in the NDN routers to further identify the IFA. The trace back method is used to prevent further attempts. The simulation results show the efficiency of the TC for mitigating the IFAs and its advantages over other typical IFA countermeasures.

We propose a secure key generation and distribution scheme for data encryption in classical optical fiber channel. A Delay interferometer (DI) is used to track the random phase fluctuation inside fiber, while the reconfigurable lengths of polarization-maintaining (PM) fiber are set as the source of optical phase fluctuations. The output signals from DI are extracted as the secret key and shared between the two-legal transmitter and receiver. Because of the randomness of local environment and the uniqueness of fiber channel, the phase fluctuation between orthogonal polarization modes (OPMs) can be used as secure keys to enhance the level of security in physical layer. Experimentally, we realize the random key generation and distribution over 25-km standard single-mode fiber (SSMF). Moreover, the proposed key generation scheme has the advantages of low cost, compatible with current optical fiber networks and long distance transmission with optical amplifiers.

We report a an experimental study of device-independent quantum random number generation based on an detection-loophole free Bell test with entangled photons. After considering statistical fluctuations and applying an 80 Gb × 45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits/s, with a failure probability less than 10-5.

When Bitcoin was first introduced to the world in 2008 by an enigmatic programmer going by the pseudonym Satoshi Nakamoto, it was billed as the world's first decentralized virtual currency. Offering the first credible incarnation of a digital currency, Bitcoin was based on the principal of peer to peer transactions involving a complex public address and a private key that only the owner of the coin would know. This paper will seek to investigate how the usage and value of Bitcoin is affected by current events in the cyber environment. Is an advancement in the digital security of Bitcoin reflected by the value of the currency and conversely does a major security breech have a negative effect? By analyzing statistical data of the market value of Bitcoin at specific points where the currency has fluctuated dramatically, it is believed that trends can be found. This paper proposes that based on the data analyzed, the current integrity of the Bitcoin security is trusted by general users and the value and usage of the currency is growing. All the major fluctuations of the currency can be linked to significant events within the digital security environment however these fluctuations are beginning to decrease in frequency and severity. Bitcoin is still a volatile currency but this paper concludes that this is a result of security flaws in Bitcoin services as opposed to the Bitcoin protocol itself.

The manufacturing process of electrical machines influences the geometric dimensions and material properties, e.g. the yoke thickness. These influences occur by statistical variation as manufacturing tolerances. The effect of these tolerances and their potential impact on the mechanical torque output is not fully studied up to now. This paper conducts a sensitivity analysis for geometric and material parameters. For the general approach these parameters are varied uniformly in a range of 10 %. Two dimensional finite element analysis is used to simulate the influences at three characteristic operating points. The studied object is an internal permanent magnet machine in the 100 kW range used for hybrid drive applications. The results show a significant dependency on the rotational speed. The general validity is studied by using boundary condition variations and two further machine designs. This procedure offers the comparison of matching qualitative results for small quantitative deviations. For detecting the impact of the manufacturing process realistic tolerance ranges are used. This investigation identifies the airgap and magnet remanence induction as the main parameters for potential torque fluctuation.

Techno-stress has been a problem in recent years with a development of information technology. Various studies have been reported about a relationship between key typing and psychosomatic state. Keystroke dynamics are known as dynamics of a key typing motion. The objective of this paper is to clarify the mechanism between keystroke dynamics and physiological responses. Inter-stroke time (IST) that was the interval between each keystroke was measured as keystroke dynamics. The physiological responses were heart rate variability (HRV) and respiration (Resp). The system consisted of IST, HRV, and Resp was applied multidimensional directed coherence in order to reveal a causal correlation. As a result, it was observed that strength of entrainment of physiological responses having fluctuation to IST differed in surround by the noise and a cognitive load. Specifically, the entrainment became weak as a cognitive resource devoted to IST was relatively increased with the keystroke motion had a robust rhythm. On the other hand, the entrainment became stronger as a cognitive resource devoted to IST was relatively decreased since the resource also devoted to the noise or the cognitive load.