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28 August 2021, Volume 26 Issue 4 Previous Issue   
Automation Technology
UAV Task Allocation for Hierarchical Multiobjective Optimization in Complex Conditions Using Modified NSGA-III with Segmented Encoding
JIN Yudong (靳宇栋), FENG Jiabo (冯家波), ZHANG Weijun (张伟军)
2021, 26 (4):  431-445.  doi: 10.1007/s12204-021-2269-5
Abstract ( 136 )   PDF (2668KB) ( 191 )  
With the recent boom in unmanned aerial vehicle (UAV) technology, many UAV applications involving complex and risky tasks in military and civilian fields have emerged, such as military strikes and disaster monitoring. Task allocation for UAVs is the process of planning the division of work among UAVs, controlled from ground stations by human operators. This study formulates the UAV task-allocation problem as an extended traveling salesman problem and presents a novel UAV task-allocation model for complex air concentration monitoring tasks. Then, an optimized non-dominated sorting genetic algorithm III (NSGA-III) based on a twin-exclusion mechanism, hierarchical objective-domination operator, and segmented gene encoding (i.e., NSGA-III-TEHOD) is developed to solve complex task-allocation problems involving multiple UAVs, hierarchical objectives, obstacles, and ambient wind. The algorithm is tested in several simulations, and the results demonstrate that the new algorithm outperforms NSGA-III, non-dominated sorting genetic algorithm II (NSGA-II), and genetic algorithm (GA) in terms of efficiency of global convergence and early maturation prevention and is available for the hierarchical objective-optimization problems.

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Velocity-Varying Target Tracking of Mobile Sensor Network Based on Flocking Control
ZHANG Lulu (章露露), DONG Xiangxiang (董祥祥), YAO Lixiu (姚莉秀), CAI Yunze (蔡云泽)
2021, 26 (4):  446-453.  doi: 10.1007/s12204-021-2283-7
Abstract ( 85 )   PDF (481KB) ( 45 )  
Existing coupled distributed estimation and motion control strategies of mobile sensor networks present limitations in velocity-varying target tracking. Therefore, a velocity-varying target tracking algorithm based on flocking control is proposed herein. The Kalman-consensus filter is utilized to estimate the position, velocity and acceleration of a target. The flocking control algorithm with a velocity-varying virtual leader enables the position of the center of the mobile sensor network to converge to that of the target. By applying an effective cascading Lyapunov method, stability analysis is performed. Simulation results are provided to validate the feasibility of the proposed algorithm.

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Fabrication and Performance Investigation of Karma Alloy Thin Film Strain Gauge
LEI Peng (雷鹏), ZHANG Congchun (张丛春), PANG Yawen (庞雅文), YANG Shenyong (杨伸勇), ZHANG Meiju (张梅菊)
2021, 26 (4):  454-462.  doi: 10.1007/s12204-021-2315-3
Abstract ( 71 )   PDF (1164KB) ( 30 )  
Karma alloy thin film strain gauges were fabricated on alumina substrates by magnetron sputtering. The electrical properties of strain gauges annealed at different temperatures were then tested. The surface morphology and phase structure of the Karma alloy thin films were analyzed using X-ray diffraction and scanning electron microscopy. The effect of the annealing temperature on the performance of the Karma alloy thin film strain gauge was also investigated. As the annealing temperature increased, it was found that the resistivity of the thin films decreased, whereas the temperature coefficient of resistance (TCR) of the thin films increased. A Karma alloy thin film strain gauge was annealed at 200 °C, thereby obtaining a gauge factor of 1.7 and a corresponding TCR of 64.8 × 10-6 K-1. The prepared Karma alloy thin film strain gauge had a lower TCR than other strain gauges at room temperature. This result can provide a reference for the preparation and application of Karma alloy thin film strain gauges in specific scenarios.
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Computer & Communication Engineering
Novel Data Placement Algorithm for Distributed Storage System  Based on Fault-Tolerant Domain
SHI Lianxing (石连星), WANG Zhiheng (王志恒), LI Xiaoyong (李小勇)
2021, 26 (4):  463-470.  doi: 10.1007/s12204-020-2253-5
Abstract ( 61 )   PDF (1240KB) ( 28 )  
The 3-replica redundancy strategy is widely used to solve the problem of data reliability in large-scale distributed storage systems. However, its storage capacity utilization is only 33%. In this paper, a data placement algorithm based on fault-tolerant domain (FTD) is proposed. Owing to the fine-grained design of the FTD, the data reliability of systems using two replicas is comparable to that of current mainstream systems using three replicas, and the capacity utilization is increased to 50%. Moreover, the proposed FTD provides a new concept for the design of distributed storage systems. Distributed storage systems can take FTDs as the units for data placement, data migration, data repair and so on. In addition, fault detection can be performed independently and concurrently within the FTDs.

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Universal Software Architecture of Magnetic Resonance-Guided Focused Ultrasound Surgery System and Experimental Study
ZHANG Shengfa (张胜发), TANG Na (唐纳), SHEN Guofeng (沈国峰), WANG Han (王悍), QIAO Shan (乔杉)
2021, 26 (4):  471-481.  doi: 10.1007/s12204-021-2325-1
Abstract ( 52 )   PDF (3241KB) ( 20 )  
Magnetic resonance-guided focused ultrasound surgery (MRgFUS) is an emerging, non-invasive hyperthermia technology which can be used for the treatment of benign and malignant tumours, in conjunction with intracranial neurological diseases. To treat different indications, it is often necessary to design special focused ultrasound devices and treatment plans, which poses great challenges and results in substantial costs during software development. This article introduces a general software architecture that can be applied to three different focused ultrasound devices for the treatment of uterine fibroids, breast fibroids, and pain palliation of bone metastases, respectively, and can be integrated with GE Discovery or Signa MRI scanners and Xingaoyi BroadScan MRI scanners. Finally, the proposed software architecture was shown to possess desirable universality and safety through various tests and animal experimental studies.

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Bearing Incipient Fault Detection Method Based on Stochastic Resonance with Triple-Well Potential System
LIU Ziwen (刘子文), XIAO Lei (肖雷), BAO Jinsong (鲍劲松), TAO Qingbao (陶清宝)
2021, 26 (4):  482-487.  doi: 10.1007/s12204-020-2238-4
Abstract ( 69 )   PDF (926KB) ( 17 )  
Bearing incipient fault characteristics are always submerged in strong background noise with weak fault characteristics, so that the incipient fault is hard to detect. Stochastic resonance (SR) is accepted to be an effective way to detect the incipient; however, output saturation may occur if bistable SR is adopted. In this paper, a bearing incipient fault detection method is proposed based on triple-well potential system and SR mechanism. The achievement of SR highly replays on the nonlinear system which is adopted a triple-well potential function in this paper. Therefore, the parameters in the nonlinear system are optimized by particle swarm optimization algorithm, and the objective of optimization is to maximize the signal-to-noise ratio of the fault signal. After optimization, the optimal system parameters are obtained thereby the resonance effect is generated and the bearing incipient fault characteristic is enhanced. The proposed method is validated by simulation verification and engineering application. The results show that the method is effective to detect an incipient signal from heavy background noise and can obtain better outputs compared with bistable SR.

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A Novel Image Encryption Technique Based on Inter Block Difference
THAMANAM Srinivasa Rao , NAGARAJA Potti, NAIK B Balaji, MANJUNATHACHARI K
2021, 26 (4):  488-493.  doi: 10.1007/s12204-021-2318-0
Abstract ( 61 )   PDF (1032KB) ( 19 )  
Data security plays a vital role in the current scenario due to the advanced and sophisticated data access techniques. Present development in data access is always a threat to data that are stored in electronic devices. Among all the forms of data, image is an important aspect that still needs methodologies to be stored securely. This work focuses on a novel technique to secure images using inter block difference and advanced encryption standard (AES). The AES algorithm is chosen for encryption since there is no prevalent attack that is successful in analyzing it. Instead of encrypting the entire image, only a part of the image is encrypted. The proposed work is found to reduce the encryption overhead in a significant way and at the same time preserves the safety of the image. It is also observed that the decryption is done in an efficient and time preserving manner.

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Word Embedding Bootstrapped Deep Active Learning Method to Information Extraction on Chinese Electronic Medical Record
MA Qunsheng (马群圣), CEN Xingxing (岑星星), YUAN Junyi (袁骏毅), HOU Xumin (侯旭敏)
2021, 26 (4):  494-502.  doi: 10.1007/s12204-021-2285-5
Abstract ( 77 )   PDF (696KB) ( 12 )  
 Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.

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Objective Evaluation of Fabric Flatness Grade Based on Convolutional Neural Network
ZHAN Zhu (占竹), ZHANG Wenjun (张文俊), CHEN Xia (陈霞), WANG Jun (汪军)
2021, 26 (4):  503-510.  doi: 10.1007/s12204-020-2239-3
Abstract ( 70 )   PDF (1870KB) ( 14 )  
 As an important indicator for the appearance and intrinsic quality of textiles, fabric flatness is the immediate cause affecting the aesthetic appearance and performance of textiles. In this paper, the objective evaluation system of fabric flatness based on 3D scanner and convolutional neural network (CNN) is constructed by using the height data of AATCC flatness template. The 3D scanner is responsible for the collection of the height value data of the sample. The effect of different sub-sample cutting sizes, cutting offsets, and network model depths on the objective evaluation coincidence rate of multiple flatness level was studied. The experimental results show that the coincidence rate of the system reaches 98.9% when the collected sample data are cut into subsamples of 20 pixel×20 pixel with 12 pixel cutting offsets and the 11-layer network model is selected. Finally, this scheme is used to evaluate the flatness of four real fabrics with different colors and textures. The result shows
that all of the samples can achieve a higher coincidence rate, which further verifies the adaptability and stability of the objective evaluation system constructed in this paper for fabric flatness evaluation.

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Dynamical Self-Reconfigurable Mechanism for Data-Driven Cell Array
SHAN Rui (山蕊), JIANG Lin (蒋林), WU Haoyue (吴昊玥), HE Feilong (贺飞龙), LIU Xinchuang (刘新闯)
2021, 26 (4):  511-521.  doi: 10.1007/s12204-021-2319-z
Abstract ( 56 )   PDF (811KB) ( 12 )  
The utilization of computation resources and reconfiguration time has a large impact on reconfiguration system performance. In order to promote the performance, a dynamical self-reconfigurable mechanism for datadriven cell array is proposed. Cells can be fired only when the needed data arrives, and cell array can be worked on two modes: fixed execution and reconfiguration. On reconfiguration mode, cell function and data flow direction are changed automatically at run time according to contexts. Simultaneously using an H-tree interconnection network, through pre-storing multiple application mapping contexts in reconfiguration buffer, multiple applications can execute concurrently and context switching time is the minimal. For verifying system performance, some algorithms are selected for mapping onto the proposed structure, and the amount of configuration contexts and execution time are recorded for statistical analysis. The results show that the proposed self-reconfigurable mechanism can reduce the number of contexts efficiently, and has a low computing time.
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Adaptive Agent-Based Modeling Framework for Collective Decision-Making in Crowd Building Evacuation
CHEN Feier (陈飞儿), ZHAO Qiyuan (赵祺源), CAO Mingming (曹明明), CHEN Jiayi (陈嘉屹), FU Guiyuan (傅桂元)
2021, 26 (4):  522-533.  doi: 10.1007/s12204-021-2287-3
Abstract ( 77 )   PDF (2086KB) ( 10 )  
Crowd evacuation in different situations is an important topic in the research field of safety. This paper presents a hybrid model for heterogeneous pedestrian evacuation simulation. Our adaptive agent-based model (ABM) combines the strength of human crowd behavior description from classical social force models with discrete dynamics expression from cellular automaton models by extending the conception of floor field. Several important factors which may influence the results of decision-making of pedestrians are taken into consideration, such as the location of sign, the attraction of exit, and the interaction among pedestrians. To compare the effect of information on the pedestrians, we construct three decision-making mechanisms with different assumptions. To validate these three simulation models, we compare the numerical results from different perspectives with rational range in the case study where the Tampere Theater evacuation was carried out. The ABM framework is open for rules modification and could be applied to different building plans and has implication for architectural design of gates and signs in order to increase the evacuation efficiency.

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Binary-Sequence Frequency Hopping Communication Method Based on Pseudo-Random Linear Frequency Modulation
TANG Zhiqiang (唐志强), QUAN Houde (全厚德), SUN Huixian (孙慧贤), CUI Peizhang (崔佩璋)
2021, 26 (4):  534-542.  doi: 10.1007/s12204-020-2250-8
Abstract ( 57 )   PDF (949KB) ( 11 )  
To improve the signal detection performance of binary-sequence frequency hopping communication when the complementary channel is jammed, a binary-sequence frequency hopping communication system based on pseudo-random liner frequency modulation (LFM) is proposed. The transmitting end uses the chirp signal to carry out the in-band spread spectrum of the binary-sequence frequency hopping signal, and then sends it out through the radio frequency front end. At the receiving end, the received signal is dehopped and processed by fractional Fourier transform. The source information is obtained by sampling decision. Firstly, a binarysequence frequency hopping system model based on pseudo-random LFM is constructed. Secondly, the bit error rate expression of anti-partial band jamming and follower jamming under the Rice channel is derived. The results show that this method has at least 5 dB performance gain than binary sequence frequency hopping for different parameter settings under partial band jamming and follower jamming, and the anti-jamming performance is significantly better than the conventional frequency hopping communication.

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Distribution-Transformed Network for Impulse Noise Removal
LI Guanyu (李冠玉), ZHANG Fengqin (张凤芹), LIU Qiegen (刘且根)
2021, 26 (4):  543-553.  doi: 10.1007/s12204-020-2203-2
Abstract ( 63 )   PDF (5279KB) ( 12 )  
This work aims to explore the restoration of images corrupted by impulse noise via distributiontransformed network (DTN), which utilizes convolutional neural network to learn pixel-distribution features from noisy images. Compared with the traditional median-based algorithms, it avoids the complicated pre-processing procedure and directly tackles the original image. Additionally, different from the traditional methods utilizing the spatial neighbor information around the pixels or patches and optimizing in an iterative manner, this work turns to capture the pixel-level distribution information by means of wide and transformed network learning. DTN fits the distribution at pixel-level with larger receptions and more channels. Furthermore, DTN utilities a residual block without batch normalization layer to generate a good estimate. In terms of edge preservation and noise suppression, the proposed DTN consistently achieves significantly superior performance than current state-of-the-art methods, particularly at extreme noise densities.
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High-Speed Fault-Tolerant Finite Impulse Response Digital Filter on Field Programmable Gate Array
WU Tao (吴焘)
2021, 26 (4):  554-558.  doi: 10.1007/s12204-020-2214-z
Abstract ( 58 )   PDF (720KB) ( 15 )  
Some fast finite impulse response (FIR) filters use a large number of look-up tables (LUTs) to configure distributed random-access memories (RAMs) and save registers. The distributed RAMs store 2M precomputed sums of M permuted operands in order to simplify the accumulation, which lays similarity to the solution of Boolean satisfiability (SAT) problem. In this work, a high-speed fault-tolerant FIR digital filter on field programmable gate array (FPGA) is proposed for hardware implementation. A shift register and an RAM are used to arrange the data flow. Generally, an N-tap digital filter only requires N embedded multipliers on FPGA. The better performance is due to high-radix words and low-latency operations. A 32-tap 8-bit FIR digital filter enjoys a throughput of 9.17MB/s, taking 109 ns to calculate one convolution. In addition, a fault-tolerant scheme by majority logic is used to correct real-time errors within digital filters.

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