The low-frequency radiated sound field can be effectively controlled through the adaptive active control method in theory. However, its application in underwater radiated noise control is not wide. In the active control system, especially the multi-channel feedback system, the step size has a very tremendous influence on the performance of the adaptive filter. If the step size is set unreasonably, the calculation results will not converge. The appropriate step size varies from case to case. For simple cases, the empirical value can be adopted to set the step size. When the numerical difference between channels is large, and when the control physical quantity such as sound pressure changes greatly with time, an determined step length can t meet the control requirements. In particular, it is difficult to choose the step size when the accurate reference signal cannot be obtained. The application of adaptive active methods in underwater noise control is limited to some extent by this problem. To solve this problem, this essay carried out the research of Filtered-X Least Mean Squares (FxLMS) algorithm based on variable step size, and carried out the corresponding numerical analysis and pool experiment to verify the feasibility of applying to underwater noise control.

With the development of streaming media, soft real-time system in today’s life could participate in the use of more extensive areas. The use frequency was also increasing. Consequently, modern processors were equipped with software control mechanisms such as DVFS (Dynamic Voltage Frequency Scaling) to allow operating systems to meet required performance while reducing power consumption. Therefore, we propose a task scheduling algorithm combined DVFS technology and time deterministic cyclic scheduling to achieve energy saving effect. First, the algorithm needed to minimize the preemption between tasks to reduce latency, so we created a buffer to save periodic tasks to avoid preemption. Second, to reduce the computational cost of the scheduling scheme, a scheduling template were designed to perform tasks. In this paper, the scheduling of periodic tasks, task scheduling would be designed when the task scheduling template would be fixed length. Besides, this algorithm supported that task could adopt appropriate voltage and frequency through DVFS technology in idle time under the condition of satisfying task dependence. Experimental analysis showed that the proposed algorithm could effectively reduce the system energy consumption while ensuring the completion of the task.

Message-locked Encryption (MLE) is the most common approach used in encrypted deduplication systems. However, the systems based on MLE are vulnerable to frequency analysis attacks, because MLE encrypts the identical plaintexts into the identical ciphertexts, which is deterministic. The state-of-theart defense scheme, which named TED, lacks key verification and uses a single key server to record frequency information. Once the key server is compromised, TED will be vulnerable to brute-force attacks. In addition, TED’s key generation algorithm needs to be designed more exquisitely to strengthen protection, and its security indicator is not comprehensive. We propose SDAF, which supports key verification and enhanced protection against frequency analysis attacks. Based on chameleon hash, SDAF realizes key verification to prevent malicious key servers from generating fake encryption keys. In order to disturb the frequency information, SDAF introduces reservoir sample to generate uniformly distributed encryption keys, and uses multiple key servers, which interact with each other via multi-party PSI and rotate spontaneously to avoid the single point of failure. Moreover, a new indicator Kurtosis is pointed out to evaluate the security against frequency analysis attacks. We implement the prototypes of SDAF. The experiments of the real-world data sets show that, compared with the existing schemes, SDAF can better resist frequency analysis attacks with lower time overheads.

Frequency hopping (FH) technology is one of the most effective technologies in the field of radio countermeasures, meanwhile, the recognition of FH signal has become a research hotspot. FH signal is a typical non-stationary signal whose frequency varies nonlinearly with time and the time-frequency analysis technique provides a very effective method for processing this kind of signal. With the renaissance of deep learning, methods based on time-frequency analysis and deep learning are widely studied. Although these methods have achieved good results, the recognition accuracy still needs to be improved. Through the observation of the datasets, we found that there are still difficult samples that are difficult to identify. Through further analysis, we propose a horizontal spatial attention (HSA) block, which can generate spatial weight vector according to the signal distribution, and then readjust the feature map. The HSA block is a plug-and-play module that can be integrated into common convolutional neural network (CNN) to further improve their performance and these networks with HSA block are collectively called HANets. The HSA block also has the advantages of high recognition accuracy (especially under low SNRs), easy to implant, and almost no influence on the number of parameters. We verified our method on two datasets and a series of comparative experiments show that the proposed method achieves good results on FH datasets.

Inertia plays a key role in power system resistance to active power disturbance. Under the background of large-scale renewable energy participating in power systems, the problem of weak inertia support brings challenges to power system security and stability operation. Based on the analysis of system equivalent inertia time constant, the inertia time constant of renewable energy access to the system in different scenarios are solved in this paper. According to the effects of inertia time constant change on the dynamic characteristics of system frequency, the assessment indexes of equivalent inertia time constant and the rate of change of frequency (RoCoF) is proposed. Then the inertia of high proportional renewable energy system and frequency stability is evaluated, combined with the assessment index of frequency deviation. Finally, the maximum renewable energy penetration of the system is analyzed with the proposed indexes. IEEE 30-bus system is used to verify the effectiveness of the proposed method by analyzing the RoCoF and equivalent inertia time constant assessment indexes.

Round-trip transmission scheme is one of key scheme for the high-precise fiber time synchronization system. Here an asymmetric channel attack against practical roundtrip time synchronization system is proposed and experimentally demonstrated. Using the achieved asymmetric channel attack module, the accuracy of the time synchronization system can be reduced from 90 ps to 538 ps as designed. It shows that channel symmetry assumption in practical applications could be broken by such attack method, and this attack could not be found without single-way-delay monitoring.

Mechanical vibration signals of GIS equipment are important information to reflect the operating status of equipment, but the vibration excitation of existing research is mostly based on a single power frequency current, and the detection effect has certain limitations. Therefore, in order to explore the influence of current frequency on GIS mechanical vibration characteristics, this paper carried out research on GIS mechanical vibration characteristics under variable frequency current excitation. Firstly, the mechanical vibration simulation platform of 110 kV GIS equipment under variable frequency current excitation was built in the laboratory. Then, the vibration signals generated by the equipment shell under normal operation state were collected based on the mechanical vibration detection system. Finally, the evolution laws of time domain and frequency domain vibration spectra of GIS equipment under different current frequencies and loads were studied. The results show that the overall time domain waveforms are smooth and the main vibration frequencies are twice the frequencies of excitation currents. Under the condition of the variable frequency current excitation with the same amplitude, the amplitudes of time domain and frequency domain vibration spectra of vibration signals are the largest when the GIS equipment is excited by 1200 A current at 40 Hz and 2400 A current at 80 Hz. Under the condition of the variable amplitude currents excitation with the same frequency, the amplitudes of vibration signals are positively correlated with the amplitudes of currents, and the distributions of frequency spectra are highly concentrated.

Large-scale renewable energy participates in the power grid through power electronic equipment, which cannot provide stable and effective inertia support for the power system. Based on the rate of change of frequency at the time of disturbance and the virtual inertia control of the energy storage system, the supporting effect of the energy storage on the inertia of a high-proportional renewable energy system is analyzed in this paper. Then an energy storage capacity configuration calculation method is proposed considering the equivalent inertia time constant and virtual inertia control parameters. Next, the quantitative analysis index is proposed based on the supporting effect of inertia, which provides analysis methods for renewable energy participating in the power grid and energy storage capacity configuration. Finally, the IEEE 30-bus system is used to analyze system frequency response characteristics under different energy storage capacity configuration scenarios. The effectiveness of the proposed method is verified.

The paper presents the stages of constructing a highly informative digital image of the time-frequency representation of information signals of cyber-physical systems. Signal visualization includes the stage of displaying the signal on the frequency-time plane, the stage of two-dimensional digital filtering and the stage of extracting highly informative components of the signal image. The use of two-dimensional digital filtering allows you to select the most informative component of the image of a complex analyzed information signal. The obtained digital image of the signal of the cyber-physical system is a highly informative initial information for solving a wide range of different problems of information security systems in cyberphysical systems with the subsequent use of machine learning technologies.

This paper studies a power conversion system supplying a High-Speed Permanent Magnet Motor (HSPMM). In opposite of classical approach, this study observes a dynamic trajectory modelling an electric drive chain with a constant acceleration of the machine to its nominal speed. This global approach allows to observe different phenomena at the same time (resonance, subharmonic, and harmonic distortion - THD) specific to the trajectory. The method reconciles electrical phenomena with a powerful mechanism of analysis from the Short-Time Fourier Transform (STFT) and the visual representation of the frequency spectrum (spectrogram tool). The Predictive Time-Frequency analysis applied on Electric Drive Systems (PreTiFEDS) offers a powerful tool for engineers and electric conversion system architects when designing the drive system chain.

Cloud computing is a unified management and scheduling model of computing resources. To satisfy multiple resource requirements for various application, edge computing has been proposed. One challenge of edge computing is cross-domain data security sharing problem. Ciphertext policy attribute-based encryption (CP-ABE) is an effective way to ensure data security sharing. However, many existing schemes focus on could computing, and do not consider the features of edge computing. In order to address this issue, we propose a cross-domain data security sharing approach for edge computing based on CP-ABE. Besides data user attributes, we also consider access control from edge nodes to user data. Our scheme first calculates public-secret key peer of each edge node based on its attributes, and then uses it to encrypt secret key of data ciphertext to ensure data security. In addition, our scheme can add non-user access control attributes such as time, location, frequency according to the different demands. In this paper we take time as example. Finally, the simulation experiments and analysis exhibit the feasibility and effectiveness of our approach.

Internet technology has made surveillance widespread and access to resources at greater ease than ever before. This implied boon has countless advantages. It however makes protecting privacy more challenging for the greater masses, and for the few hacktivists, supplies anonymity. The ever-increasing frequency and scale of cyber-attacks has not only crippled private organizations but has also left Law Enforcement Agencies(LEA's) in a fix: as data depicts a surge in cases relating to cyber-bullying, ransomware attacks; and the force not having adequate manpower to tackle such cases on a more microscopic level. The need is for a tool, an automated assistant which will help the security officers cut down precious time needed in the very first phase of information gathering: reconnaissance. Confronting the surface web along with the deep and dark web is not only a tedious job but which requires documenting the digital footprint of the perpetrator and identifying any Indicators of Compromise(IOC's). TORSION which automates web reconnaissance using the Open Source Intelligence paradigm, extracts the metadata from popular indexed social sites and un-indexed dark web onion sites, provided it has some relating Intel on the target. TORSION's workflow allows account matching from various top indexed sites, generating a dossier on the target, and exporting the collected metadata to a PDF file which can later be referenced.