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Recently, fault-aware retraining has emerged as a promising approach to improve the error resilience of Deep Neu-ral Networks (DNNs) against manufacturing-induced defects in DNN accelerators. However, state-of-the-art fault-aware training techniques incur a gigantic retraining overhead due to their per-chip retraining nature for the chip’s unique fault map, which may render it practically infeasible if retraining is done on large datasets. To address this major limitation and improve the practicability of the fault-aware retraining methodology, this work proposes a novel concept of merging fault maps to effectively retrain a DNN for a group of faulty chips in a single fault-aware retraining round. The merging of fault maps enables to avoid per chip retraining and thereby reduces the retraining overhead significantly. However, the merging of fault maps brings in new challenges such as training divergence (accuracy collapse) if a high number of accumulated faults are injected into the network in the first epoch. To address these challenges, we propose a methodology for effective merging of fault maps and then retraining of DNNs. Experimental results show that our methodology offers at least 1.4x retraining speedup on average while improving the error resilience of the network (depending on the DNN models and the number of merged fault maps). For example, for the Resnet-32 model using fault map generated from 5 fault maps at the fault rate 6e-3, our methodology offers 2x retraining speedup and 0.6% classification accuracy drop against per-chip retraining.

In this paper, we propose a digital network coding protocol to enhance data rate for the secondary network in underlay cognitive radio environment. In the proposed protocol, transmit antenna selection and selection combining techniques are respectively used by a primary transmitter and a primary receiver to enhance performance for a primary network. In a secondary network, two secondary sources exchange their information via assistance of a common secondary relay that employs interference cancellation method to reduce one time slot, as compared with the conventional digital network coding protocol. We derive expressions of transmit power of the secondary nodes under a constraint of outage probability (OP) given by the primary network. Exact and asymptotic closed-form formulas of OP of the secondary network over Rayleigh fading channel are derived, and verified by Monte-Carlo simulations.

This paper demonstrates the performance evaluation of UAV detection based on micro-Doppler radar image data with the proposed inception-residual neural network (IRNN). Accordingly, the network is designed and analyzed by changing network hyper-parameters through experiment with the Real Doppler RAD-DAR (RDRD) dataset that is collected by the practical measurements. Numerical analysis results show that the proposed network with 16 filters yield a good trade-off between accuracy and time-consuming performances. Moreover, the network is taken into account for competing with three other networks. Due to inception-residual structure, the proposed network remarkably outperforms other ones.

In recent years, with the increasing proportion of renewable energy, some system problems have gradually emerged. To reduce the economic cost of system operations and improve power system reliability, renewable power forecasting is an indispensable part. Compared with the deterministic prediction, the probabilistic forecast considers the uncertainty, which helps manage risks and make decisions for power grids. This study proposes a novel probabilistic forecasting method for wind power generation, which includes data preprocessing, adaptive neuro fuzzy inference system (ANFIS) training model with fuzzy c-means clustering algorithm, and post processing of predicted-interval. The input data of the proposed probabilistic forecasting model include the numerical weather prediction (NWP) ensemble wind speeds, NWP spot wind-speed forecasts, and historical wind power measurements. For practical applications, measured data of power generation at actual wind farms were used to compare different forecasting models. The research results demonstrate that the proposed model supports better performance and prediction stability. Furthermore, this work reveals the importance of both data preprocessing and post processing of predicted interval on wind power forecasting. These essential processes greatly improve the performance of the probabilistic wind power forecasts.

International audience; This paper considers delay systems with characteristic equation being a quasi-polynomial with one delay and polynomials of degree one. It is shown that for a subclass of systems which have a chain of poles clustering the imaginary axis by the left, the procedure of Walton and Marshall fails: we prove the existence, for an infinitesimally small delay, of a positive real pole at infinity. This real pole is then proved to be the unique pole of the system in the closed right half-plane for all values of the delay. Some numerical examples illustrate the results.

The standardization of the first phase 5G system in Release 15 named New Radio (NR) will soon move on to the stand-alone version with intention to bring it commercially to the market around 2020. This paper investigates the impact of power amplifier in OFDM-based waveforms including CP-OFDM, F-OFDM and W-OFDM. We first derive a mathematical model for these OFDM-based waveforms. Then we analyze the impact of power amplifier (PA) to the out-of-band (OOB) waveform, which is a critical component in the densely spectrum with different channel widths using scalable numerologies. It is shown that W-OFDM and F-OFDM are more suitable than the conventional CP-OFDM. The provided results of this paper thus facilitate knowledge of design and practice for further NR features. As the simulation results, without PA clipping, the W-OFDM and F-OFDM present better OOB characteristic than CP-OFDM, while applying PA clipping both of them show the similar performance. Additionally, another W-OFDM design with PA clipping shows advantages to implement for 5G NR waveform.

We analyze the H∞-stability of neutral systems with commensurate delays and multiple chains of poles asymptotic to a same set of points on the imaginary axis. First, by approximation, the location of poles of large modulus is determined. This analysis requires to consider several subclasses of systems where poles of high modulus exhibit various patterns. Second, we derive necessary and sufficient conditions for H∞-stability which are easy to check as expressed in terms of the degrees of the polynomials involved in the numerator and denominator of the transfer function.

In mobile base station antennas, linear array antennas are employed to achieve shaped beams in the vertical plane. Also, antennas are designed to achieve multi-frequency use. In the case of expanding multi-frequency range, conventional equal element spacing configuration shows a defect of producing grating lobes. In order to suppress grating lobes, unequal element spacing is promising. In this paper, a radiation pattern synthesis based on a least mean square method (LMS) is applied to unequally spaced array configurations. In frequencies of 900 MHz, 1.5 GHz, 2.0 GHz, and 2.5 GHz, calculated results are presented. Suppression of grating lobes and increase of antenna gain are shown. Also, simulation results by FEKO using electric point source and dipole as array elements are shown to confirm accuracy of this method.

There are some deficiencies such as inconsistency, oversize waist, and sharp corner in the cross-section of the welding seam. Orthogonal test is conducted under different speed, defocus, and offset. The metalLic phase of the seam is observed by using electronic microscope. By analyzing the results of Erichsen test and tensile test, the effect of processing parameters on the performance of the seam is studied. The results show that deep punching performance will be improved and the waist will be reduced when the welding speed is increased. If the defocus is a quarter of the thick blank thickness and the offset value is 0.1mm, the surface of the welding seam will be smooth.

A general statement of face recognition problem (in computer vision) can be formulated as follows: Given still or video images of a scene, identify or verify one or more persons in the scene using a database of stored faces. For the fully automatic system of face recognition, a lot of researches have been done in many directions that eliminate the difficulties of finding and identifying faces with the illumination and pose problems. However, most of them just concentrate on these issues separately instead of consider the efficiency of the whole system while both method in face detection and recognition work together. This research tried to make compensation between those two main components. In the recognition step, eigenface technique was used to normalize faces in the database. At first, we calculate the face covariance matrix and the associated face eigenvectors. These will be the face prints. Different vectors of weights can represent different faces. After that, any face can be reconstructed from a set of weights. So we can recognize a new picture of a familiar face. For the face detection process, skin-color model was used to separate skin areas from the background in order to cut out the face block for recognizing. The whole process of face recognition can be separate into two phases: Training phase and Recognition phase. At first, we train the recognition system by using a sequence of images for each person to create the alternative face print. In the second phase, the result of the first phase will be used to match with the input face image in the video stream and then show the conclusion on whether people in video is granted or not. The experiments included both aspects: independent component and holistic system on the popular databases of face detection, recognition in still image and video. The result achieved a recognition rate at from 80 percents to 100 percents with a small number of 10 or 24 training samples.