When a failure occurs in a network element, such as switch, router, and server, network operators need to recognize the service impact, such as time to recovery from the failure or severity of the failure, since service impact is essential information for handling failures. In this paper, we propose Deep learning based Service Impact Prediction system (DeepSIP), which predicts the service impact of network failure in a network element using a temporal multimodal convolutional neural network (CNN). More precisely, DeepSIP predicts the time to recovery from the failure and the loss of traffic volume due to the failure in a network on the basis of information from syslog messages and traffic volume. Since the time to recovery is useful information for a service level agreement (SLA) and the loss of traffic volume is directly related to the severity of the failure, we regard the time to recovery and the loss of traffic volume as the service impact. The service impact is challenging to predict, since it depends on types of network failures and traffic volume when the failure occurs. Moreover, network elements do not explicitly contain any information about the service impact. To extract the type of network failures and predict the service impact, we use syslog messages and past traffic volume. However, syslog messages and traffic volume are also challenging to analyze because these data are multimodal, are strongly correlated, and have temporal dependencies. To extract useful features for prediction, we develop a temporal multimodal CNN. We experimentally evaluated DeepSIP in terms of accuracy by comparing it with other NN-based methods by using synthetic and real datasets. For both datasets, the results show that DeepSIP outperformed the baselines.

Refinement-based formal specification is a promising approach to the increasing complexity of software systems, as demonstrated in the formal method Event-B. It allows stepwise modeling and verifying of complex systems with multiple steps at different abstraction levels. However, making changes is more difficult, as caution is necessary to avoid breaking the consistency between the steps. Judging whether a change is valid or not is a non-trivial task, as the logical dependency relationships between the modeling elements (predicates) are implicit and complex. In this paper, we propose a method for analyzing the impact of the changes of Event-B. By attaching labels to modeling elements (predicates), the method helps engineers understand how a model is structured and what needs to be modified to accomplish a change.

Cloud computing has attracted a growing number of enterprises to move their business to the cloud because of the associated operational and cost benefits. Improving availability is one of the major concerns of cloud application owners because modern applications generally comprise a large number of components and failures are common at scale. Fault tolerance enables an application to continue operating properly when failure occurs, but fault tolerance strategy is typically employed for the most important components because of financial concerns. Therefore, identifying important components has become a critical research issue. To address this problem, we propose a failure-sensitive structure-based component ranking approach (FSCRank), which integrates component failure impact and application structure information into component importance evaluation. An iterative ranking algorithm is developed according to the structural characteristics of cloud applications. The experimental results show that FSCRank outperforms the other two structure-based ranking algorithms for cloud applications. In addition, factors that affect application availability optimization are analyzed and summarized. The experimental results suggest that the availability of cloud applications can be greatly improved by implementing fault tolerance strategy for the important components identified by FSCRank.

In this research, we propose an effective stability analysis method to impacting systems with periodically moving borders (periodic borders). First, we describe an n-dimensional impacting system with periodic borders. Subsequently, we present an algorithm based on a stability analysis method using the monodromy matrix for calculating stability of the waveform. This approach requires the state-transition matrix be related to the impact phenomenon, which is known as the saltation matrix. In an earlier study, the expression for the saltation matrix was derived assuming a static border (fixed border). In this research, we derive an expression for the saltation matrix for a periodic border. We confirm the performance of the proposed method, which is also applicable to systems with fixed borders, by applying it to an impacting system with a periodic border. Using this approach, we analyze the bifurcation of an impacting system with a periodic border by computing the evolution of the stable and unstable periodic waveform. We demonstrate a discontinuous change of the periodic points, which occurs when a periodic point collides with a border, in the one-parameter bifurcation diagram.

In a speech enhancement system for impact noise, it is important for any impact noise activity to be detected. However, because impact noise occurs suddenly, it is not always easy to detect. We propose a method for impact noise activity detection based on the kurtosis of an instantaneous power spectrum. The continuous duration of a generalized impact noise is shorter than that of speech, and the power of such impact noise varies dramatically. Consequently, the distribution of the instantaneous power spectrum of impact noise is different from that of speech. The proposed detection takes advantage of kurtosis, which depends on the sharpness and skirt of the distribution. Simulation results show that the proposed noise activity detection improves the performance of the speech enhancement system.

The indexes of the degradation of C/N, ΔT/T and I/N, which can be converted from one to another, are used to evaluate the impact of interference on the satellite link. However, it is not suitable to intuitively understand how these parameters degrade the quality of services. In this paper, we propose to evaluate the impact of interference on the performance of BSS (Broadcasting Satellite Services) in terms of the increase rate of the outage time caused by the rain attenuation. Some calculation results are given for the 12GHz band BSS in Japan.

We propose an impact and high-pitch noise-suppression method based on spectral entropy. Spectral entropy takes a large value for flat spectral amplitude and a small value for spectra with several lines. We model the impact noise as a flat spectral signal and its damped oscillation as a high-pitch periodic signal consisting of spectra with several lines. We discriminate between the current noise situations by using spectral entropy and adaptively change the noise-suppression parameters used in a zero phase-based impact-noise-suppression method. Simulation results show that the proposed method can improve the perceptual evaluation of the speech quality and speech-recognition rate compared to conventional methods.

This paper proposes a noise reduction method for impact noise with damped oscillation caused by clinking a glass, hitting a bottle, and so on. The proposed method is based on the zero phase (ZP) signal defined as the IDFT of the spectral amplitude. When the target noise can be modeled as the sum of the impact part and the damped oscillation part, the proposed method can reduce them individually. First, the proposed method estimates the damped oscillation spectra and subtracts them from the observed spectra. Then, the impact part is reduced by replacing several samples of the ZP observed signal. Simulation results show that the proposed method improved 10dB of SNR of real impact noise.

Requirements changes frequently occur at any time of a software development process, and their management is a crucial issue to develop software of high quality. Meanwhile, goal-oriented analysis techniques are being put into practice to elicit requirements. In this situation, the change management of goal graphs and its support are necessary. This paper presents a technique related to the change management of goal graphs, realizing impact analysis on a goal graph when its modifications occur. Our impact analysis detects conflicts that arise when a new goal is added, and investigates the achievability of the other goals when an existing goal is deleted. We have implemented a supporting tool for automating the analysis. Two case studies suggested the efficiency of the proposed approach.

Feature location is to identify source code that implements a given feature. It is essential for software maintenance and evolution. A large amount of research, including static analysis, dynamic analysis and the hybrid approaches, has been done on the feature location problems. The existing approaches either need plenty of scenarios or rely on domain experts heavily. This paper proposes a new approach to locate functional feature in source code by combining the change impact analysis and information retrieval. In this approach, the source code is instrumented and executed using a single scenario to obtain the execution trace. The execution trace is extended according to the control flow to cover all the potentially relevant classes. The classes are ranked by trace-based impact analysis and information retrieval. The ranking analysis takes advantages of the semantics and structural characteristics of source code. The identified results are of higher precision than the individual approaches. Finally, two open source cases have been studied and the efficiency of the proposed approach is verified.

The impact degree is a measure of the robustness of a metabolic network against deletion of single or multiple reaction(s). Although such a measure is useful for mining important enzymes/genes, it was defined only for networks without cycles. In this paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm to calculate the impact degree. Furthermore we improve this algorithm to reduce computation time for the impact degree by deletions of multiple reactions. We applied our method to the metabolic network of E. coli, that includes reference pathways, consisting of 3281 reaction nodes and 2444 compound nodes, downloaded from KEGG database, and calculate the distribution of the impact degree. The results of our computational experiments show that the improved algorithm is 18.4 times faster than the simple algorithm for deletion of reaction-pairs and 11.4 times faster for deletion of reaction-triplets. We also enumerate genes with high impact degrees for single and multiple reaction deletions.

The extended binary phase shift keying (EBPSK) transmission system with ultra narrow bandwidth has excellent BER performance, which raises many doubts with the researchers. Therefore, on the premise of the existence of a special filter that can transform the modulated phase information into amplitude information, the theoretical BER formula of EBPSK system in Additive White Gaussian Noise (AWGN) channel has been deduced. This paper gives the theoretical values of the parameters in the above BER formula and discusses the effects of parameters on BER firstly. Then the paper shows that the special impacting filter satisfies the above assumption, therefore, in the frame of binary detection theory, the excellent performance of high-efficiency EBPSK system can be explained and the correction of the theoretical BER formula can be validated.

In this study, we have performed both the channel modification of the conventional MHEMT (Metamorphic High Electron Mobility Transistor) and the variation of gate recess width to improve the breakdown and RF characteristics. The modified channel consists of the InxGa1-xAs and the InP layers. Since InP has lower impact ionization coefficient than In0.53Ga0.47As, we have adopted the InP-composite channel in the modified MHEMT. Also, the gate recess width is both functions of breakdown and RF characteristic of a HEMT structure. Therefore, we have studied the breakdown and RF characteristic for various gate recess widths in MHEMT. We have compared breakdown characteristic of the InP-composite channel with that of conventional MHEMT. It is shown that on and off state breakdown voltages of the InP-composite channel MHEMT were increased by about 20 and 27%, respectively, compared with the conventional structure. Also, breakdown voltage of the InP-composite channel MHEMT was increased with increasing gate recess width. The fT was increased with decreasing the gate recess width, whereas fmax was increased with increasing the gate recess width. Also, we extracted small-signal parameters. It was shown that Gd of the InP-composite channel MHEMT is decreased about by 30% compared with the conventional MHEMT. Therefore, the suppression of the impact ionization in the InP-composite channel increases the breakdown voltage and decreases the output conductance.

Though microwave emissions in association with hypervelocity impacts have been confirmed experimentally, the emission characteristics and the emission mechanism have not yet been made clear. For example, whether the emission is noise or a coherent signal is not known. In this paper, two kinds of analyses, output waveform analysis and spectral analysis, are discussed. Regarding output waveform analysis, microwave pulses are simulated on the basis of a microcrack model in consideration of discharge current, and are compared with experimental pulses in terms of the pulse shape and width. Regarding spectral analysis, the intensity and phase of waveforms are calculated in the frequency domain to examine the frequency characteristics. The spectral intensity is nearly flat and the phase has coherency. Accordingly, the microwave emission is not noise but a sequence of independent pulses.

In this paper, we describe the effect of p-regions on the I-V kink in GaAs FETs. A kink-free p-pocket-type self-aligned gate GaAs MESFET (PP-MESFET), which does not include p-regions under the channel, has been analyzed and compared with a conventional buried-p-type self-aligned gate GaAs MESFET (BP-MESFET) using two-dimensional device simulation. The relation between the I-V kink and the layout of p-regions has been demonstrated by numerical simulation for the first time. For both the BP-MESFET and PP-MESFET, impact ionization produces holes in high-field regions. The holes accumulate under the channel, widen the channel, and cause an abrupt increase in drain current in turn in the BP-MESFET. On the other hand, in the PP-MESFET, holes generated in the high-field region are transported to the source region easily over the lower barrier owing to the absence of p-regions under the channel. Holes do not accumulate under the channel, leading to kink-free I-V characteristics of the PP-MESFET. P-regions should be located so as not to cause the accumulation of holes in GaAs FETs where p-regions are required for high-frequency performance.

To perform a comparative study, we experimented on two differential epitaxial structures, the conventional metamorphic high-electron-mobility-transistor (MHEMT) using the InAlAs/InGaAs/InAlAs structure and the InP-composite-channel MHEMT adopting the InAlAs/InGaAs/InP structure. Compared with the conventional MHEMT, the InP-composite-channel MHEMT shows improved breakdown performance; more than approximately 3.8 V. This increased breakdown voltage can be explained by the lower impact ionization coefficient of the InP-composite-channel MHEMT than that of the conventional MHEMT. The InP-composite-channel MHEMT also shows improved Radio Frequency characteristics of S21 gain of approximately 4.35 dB at 50 GHz, and a cutoff frequency (fT) and a maximum frequency of oscillation (fmax) of approximately 124 GHz and 240 GHz, respectively, were obtained. These are due to decreases in go and gm.

Cyberworlds are being formed in cyberspaces as computational spaces. Now cyberspaces are rapidly expanding on the Web either intentionally or spontaneously, with or without design. Widespread and intensive local activities are melting each other on the web globally to create cyberworlds. The major key players of cyberworlds include e-finance that trades a GDP-equivalent a day and e-manufacturing that is transforming industrial production into Web shopping of product components and assembly factories. Lacking proper theory and design, cyberworlds have continued to grow chaotic and are now out of human understanding and control. This research first presents a generic theoretical framework and design based on algebraic topology, and also provides an axiomatic approach to theorize the potentials of cyberworlds.

Feedforward neural networks have been successfully developed and applied in many areas because of their universal approximation capability. However, there still remains the problem of determining a suitable network structure for the given task. In this paper, we propose a novel self-organizing neural network which automatically adjusts its structure according to the task. Utilizing both the constructive and the pruning procedures, the proposed algorithm finds a near-optimal network which is compact and shows good generalization performance. One of its important features is reliability, which means the randomness of neural networks is effectively reduced. The resultant networks can have suitable numbers of hidden neurons and hidden layers according to the complexity of the given task. The simulation results for the well-known function regression problems show that our method successfully organizes near-optimal networks.

DC and RF performances of AlGaN/GaN HEMTs are simulated using a two-dimensional device simulator with the material parameters of GaN and AlGaN. The cut-off frequency is estimated as 205 GHz at the gate length of 0.05 µm and the drain breakdown voltage at this gate length is over 10 V. The values are satisfactory for millimeter wavelength power applications. The use of thin AlGaN layers has key importance to alleviate gate parasitic capacitance effects at this gate length.

Electron emissions from single-crystalline diamond surfaces by internally exciting electrons from the valence to conduction bands have been investigated. Monte Carlo simulations have been employed to estimate the impact ionization rates of carriers in diamond under high electric fields up to 1107V/cm. The calculations demonstrate substantial impact ionization rates which rapidly increase with increasing electric fields above 8105V/cm. Highly efficient electron emissions with high emission current efficiencies of approximate unity have been attained from a MIS-type diamond layered structure that are composed of heavily ion-implanted buried layer (M), undoped diamond (I) and hydrogenated p-type diamond (S) with an emission surface of a negative electron affinity. The highly efficient emission mechanism is discussed in relation to the field excitation of electrons from the valence band to the conduction band in the undoped diamond layer and the carrier transport to the diamond surface.