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FAGC: Free Space Fragmentation Aware GC Scheme based on Observations of Energy Consumption

FAGC: Free Space Fragmentation Aware GC Scheme based on Obse...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Lihua Yang Zhipeng Tan Fang Wang Yang Xiao Wei Zhang Biao He Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology College of Science and Technology National University of Defense Technology China Huawei Technology Co. Ltd China

Smartphones are everyday necessities with limited power supply. Charging a smartphone twice a day or more affects user experience. Flash friendly file system (F2FS) is a widely-used log-structured file system for smartphones. Free space fragmentation of F2FS consists of invalid blocks mainly causing performance degradation. F2FS reclaims invalid blocks by garbage collection (GC). We explore the energy consumption of GC and the effect of GC on reducing free space fragments. We observe the energy consumption of one background GC is large but its effect on reducing free space fragments is limited. These motivate us to improve the energy efficiency of GC. We reassess how much free space is a free space fragment based on data analysis, use the free space fragmentation factor to measure the degree of free space fragmentation quickly. We suggest the free space fragmentation aware GC scheme (FAGC) that optimizes the selection for victim segments and the migration for valid blocks. Experiments on real platform show that FAGC reduces GC count by 82.68% and 74.51% respectively than traditional F2FS and the latest GC optimization of F2FS, ATGC. FAGC reduces the energy consumption by 164.37 J and 100.64 J compared to traditional F2FS and ATGC respectively for a synthetic benchmark.

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A Reinforcement Learning PID Compound Protocol Design Method for Ultrasonic Motor Control*

A Reinforcement Learning PID Compound Protocol Design Method...

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Control Conference (ANZCC), Australian and New Zealand

作者： Qiang Wang Yunlong Yang Xiongjun Wu Jinqi Wang Guanyu Liu The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Aiming at overcoming drawback brought by the nonlinear and time-varying characteristics of ultrasonic motor, this paper proposed a PID compound controller of ultrasonic motor based on reinforcement learning to improve the dynamic performance and adaptability of the system. In practice, the output control value of the reinforcement learning controller is observed in general to be uncertain, and sometimes even destroys the learning strategy, which makes the system unstable. To overcome this limitation, this paper ultilized the idea of expert rules to improve the controller performance, and the Lyapunov stability is also guaranteed. The simulation results show that the improved composite control algorithm can significantly improve the learning efficiency, and meanwhile shorten the number of learning iterations. At the same time, the composite controller based on expert rules significantly improves the control performance of the system, in terms of reducing the rise time and adjustment time of the system, as well as reducing the steady-state error of the system.

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SAR Target Recognition Based on Model Transfer and Hinge Loss with Limited Data 1st

SAR Target Recognition Based on Model Transfer and Hinge Los...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： He, Qishan Zhao, Lingjun Kuang, Gangyao Liu, Li State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronics Science and Technology National University of Defense Technology Changsha China College of System Engineering National University of Defense Technology Changsha China CMVS Univeristy of Oulu Oulu Finland

ISBN: (纸本)9783030930455

Convolutional neural networks have made great achievements in field of optical image classification during recent years. However, for Synthetic Aperture Radar automatic target recognition (SAR-ATR) tasks, the performance of deep learning networks is always degraded by the insufficient size of SAR images, which cause both severe over-fitting and low-capacity feature extraction model. On the other hand, models with high feature representation ability usually lose anti-overfitting capability to a certain extent, while enhancing the network’s robustness leads to degradation in feature extraction capability. To balance above both problems, a network with model transfer using the GAN-WP and non-greedy loss is introduced in this paper. Firstly, inspired by the Support Vector Machine’s mechanism, multi-hinge loss is used during training stage. Then, instead of directly training a deep neural network with the insufficient labeled SAR dataset, we pretrain the feature extraction network by an improved GAN, called Wasserstein GAN with gradient penalty and transfer the pre-trained layers to an all-convolutional network based on the fine-tune technique. Furthermore, experimental results on the MSTAR dataset illustrate the effectiveness of the proposed new method, which additional shows the classification accuracy can be improved more largely than other method in the case of sparse training dataset. © 2021, Springer Nature Switzerland AG.

关键词： Feature extraction

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Anechoic chamber equivalent simulation method for intermittent transceiver echo based on space target electromagnetic scattering data

Anechoic chamber equivalent simulation method for intermitte...

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IET International Radar Conference (IRC 2023)

作者： Zhenyu Qiao Xiaobin Liu Ailun Xie Qihua Wu Feng Zhao Ling Wang The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology Changsha People's Republic of China

ISBN: (数字)9781837240982

Electromagnetic simulations and anechoic chamber measurements are commonly used to obtain target characteristics. The use of intermittent transceiver in anechoic chamber can effectively solve the problem of the signal coupling at the receiver. However, the method of equivalent simulation between electromagnetic simulation and intermittent transceiver is rarely discussed. In this paper, an intermittent transceiver echo equivalent simulation method in anechoic chamber is proposed based on space electromagnetic scattering data. Then, the characteristics of scattering points of targets under micro-motion conditions are analyzed. Finally, the equivalent relationship and processing flow between electromagnetic scattering data and intermittent transceiver echo are discussed. Simulations are conducted to verify the effectiveness of the proposed method.

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Counter-reconnaissance performance analysis of amplitude coded modulation radar waveform

Counter-reconnaissance performance analysis of amplitude cod...

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IET International Radar Conference (IRC 2023)

作者： Zhenyu Liu Xiaobin Liu Qihua Wu Feng Zhao Zhiming Xu Shunping Xiao The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology ChangSha People's Republic of China

ISBN: (数字)9781837240982

The electronic reconnaissance systems can obtain intelligence information from technical parameters of radiation sources. Then, anti-radiation missiles can be used to destroy radiation sources, seriously threatening the survival of radar systems. To improve radar counter-reconnaissance capability, various waveforms are proposed. Among the waveforms, amplitude coded modulation (ACM) can divide the pulse into many short pulses. The signal sorting may fail so that the signal is difficult to be recognized. Therefore, the ACM signal can improve the counter-reconnaissance performance. However, the signal counter- reconnaissance performance has not been discussed. Thus, the uniform and non-periodic ACM model are proposed in this paper. Time-frequency distribution entropy and intercept probability of ACM signal are utilized to evaluate the counter-reconnaissance performance. Simulations are conducted to verify the accuracy of theoretical analysis.

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Characteristics analysis of stepped frequency chirp signal using uniform interrupted modulation

Characteristics analysis of stepped frequency chirp signal u...

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IET International Radar Conference (IRC 2023)

作者： Tiehua Zhao Qihua Wu Zhiming Xu Feng Zhao Shunping Xiao The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology Changsha People's Republic of China

ISBN: (数字)9781837240982

The co-modulation is beneficial for increasing the anti-reconnaissance potential of radar waveform. Stepped frequency chirp (SFC) signal has the characteristics of both linear frequency modulation (LFM) signal and stepped-frequency waveform (SFW). Uniform interrupted modulation (UIM) can increase the complexity of the signal in the time domain. Therefore, the characteristics of the SFC signal with UIM are deduced in this paper. First of all, the processing method of the SFC signal is introduced. Then the characteristics of the SFC signal with UIM are analysed emphatically. The high-resolution range profile (HRRP) of the inter-pulse and intra-pulse interrupted modulation are both deduced respectively. It is concluded that the UIM between pulses brings the HRRP of the SFC signal multiple pseudo peaks extending along the range profile. However, there is no pseudo peak in the HRRP of the intra-pulse interruptedly modulated SFC signal. Signal simulation experiments are carried out with MATLAB to verify the accuracy of the theoretical analysis.

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Waveform design for pulse radar based on interrupted coding modulation

Waveform design for pulse radar based on interrupted coding ...

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IET International Radar Conference (IRC 2023)

作者： Xiaobin Liu Qihua Wu Zhaoyu Gu Zhenyu Liu Feng Zhao Shunping Xiao The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology ChangSha People's Republic of China

ISBN: (数字)9781837240982

Coding modulation is utilized to design radar waveform to obtain complex properties so that the waveform is difficult to be recognized. However, traditional coding modulation brings high sidelobe in target range profile which makes the detection performance decrease. To solve this problem, the interrupted coding modulation is proposed in this paper based on the interrupted transmitting and receiving. The signal model is provided and the low sidelobe property of range profile is discussed. Constant false-alarm rate (CFAR) is utilized to evaluate the detection performance of interrupted coding modulation waveform. Simulations are conducted to verify the low sidelobe property and detection performance of the proposed waveform.

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A ship detection method based on YOLOv7 in range-compressed SAR data

A ship detection method based on YOLOv7 in range-compressed ...

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IET International Radar Conference (IRC 2023)

作者： Xiangdong Tan Xiangguang Leng Jin Wang Kefeng Ji State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology Changsha People's Republic of China

ISBN: (数字)9781837240982

Synthetic Aperture Radar (SAR) is an exceptionally effective imaging technology capable of acquiring ground information regardless of weather conditions and time of day. SAR finds extensive applications in target detection, tracking, and recognition. Despite its wide imaging coverage and high resolution, ship target detection images face several challenges. The most prominent challenge is that both traditional methods such as constant false alarm rate (CFAR) detection and deep neural network methods require complex and time-consuming imaging processing, including but not limited to range migration correction and pulse compression processes. Consequently, real-time detection becomes arduous, leading to delayed results for subsequent applications such as target monitoring and tracking. To address this issue, this study proposes a novel ship detection method that leverages deep learning techniques along with range-compressed data from SAR images to enhance immediate target perception with a convolutional neural network algorithm. The aim is to improve the accurac y and timeliness of ship detection tasks. Given the limited availability of literature on this subject matter, this paper aims to provide an analysis of the proposed method's effectiveness through experimental results while also discussing future prospects.

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AWNet: Negative Obstacle Semantic Segmentation Network Based on Adaptive Weight Loss

AWNet: Negative Obstacle Semantic Segmentation Network Based...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Jiarui Zhao Guizhen Yu Jie Wang Zhangyu Wang Junjie Zhang Yunsong Feng State Key Lab of Intelligent Transportation System School of Transportation Science and Engineering The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Research Institute for Frontier Science Hefei innovation research institute of Beihang university Beihang University Hefei China State Key Laboratory of Pulsed Power Laser Technology National University of Defense Technology Heufei China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

On roads, both vehicles and pedestrians will leave traces after passing by. Slight traces will not cause any trouble to the passing vehicles and pedestrians subsequently. However, there are certain potholes in the road that have been formed by repeated crushing of vehicles. It may cause mechanical damage to following vehicles and lead to accidents as vehicles get stuck in the potholes. Thus, the segmentation of negative obstacles is crucial for the safe driving of self-driving vehicles. This paper proposed an AWNet neural network to this end, and quantitative analyses were conducted based on the publicly available dataset DRNO. Specifically, a random cut-and-joint module (RCJ) was proposed to augment the dataset. The crossentropy loss function was modified by adding an adaptive weight module, i.e., the adaptive weight cross-entropy loss function. It allows the model to focus on negative obstacles during implementation. Furthermore, this paper proposed a post-processing module for adaptive viewpoint conversion based on depth maps, which is capable of fusing depth maps and RGB images to convert the front view into a BEV viewpoint. The mAcc for Negative obstacle semantic segmentation can reach $78.39\%$ , the mIoU can reach $73.21{{\% }}$ , the mF 1 can reach $79.23{{\% }}$ . The experimental results reveal that the AWNet is able to state-of-the-art performance compared with other networks.

关键词： Adaptation models Pedestrians Statistical analysis Roads Semantic segmentation Semantics Neural networks Data augmentation Data models Informatics

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Suppression of Adverse Effects of Transmission Clearance in Brushless DC Motor Servo systems by Switching Compensation*

Suppression of Adverse Effects of Transmission Clearance in ...

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Control Conference (ANZCC), Australian and New Zealand

作者： Dao Zhou Yunlong Yang Xiongjun Wu Biao Liu Shanghai Aerospace Control Technology Research Institute Shanghai China The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

In conventional servo system with transmission mechanisms, the transmission backlash seems to be an inevitable phenomenon in many applications. Excessive backlash can lead to relative motion between the servo motor and the load, resulting in response delays, instability, and oscillations, consequently causing a decline in the dynamic performance of the system. To mitigate the negative effects of transmission backlash, this paper proposes a model-based backstepping adaptive control algorithm and a switching compensation approach tailored to transmission backlash. A feed-forward control gap compensation method is suggested. Leveraging identified servo system parameter information, this method achieves compensation and suppression of the nonlinearity caused by backlash. The efficacy of the proposed method is verified through theoretical analysis and simulation experiments, which also guaranteed its capability to effectively enhance the tracking accuracy and stability of the servo system while suppressing undesirable phenomena caused by backlash.

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