ISSN 
2021 Vol. 50, No. 4
2021, 50(4): 481-487.
doi: 10.12178/1001-0548.2021075
Abstract:
Deceptive jamming as a typical active jamming, produces false target on the range dimension or Doppler dimension, which increases probability of false alarm and significantly degrade the performance of radar. This paper is focused on the joint design of pulse amplitude and phase against velocity deceptive jamming. In particular, a design criterion incorporating a weighted sum of jamming energy and target sidelobe energy in the stopband is developed to minimize under phase quantized and peak-to-average power ratio (PAR) restriction. To tackle the resulting non-convex optimization problem, a new inexact alternating direction penalty method (IADPM) is proposed based on the ADPM framework and the computational complexity is analyzed. Finally, numerical results are provided to demonstrate the effectiveness of the proposed methodology.
Deceptive jamming as a typical active jamming, produces false target on the range dimension or Doppler dimension, which increases probability of false alarm and significantly degrade the performance of radar. This paper is focused on the joint design of pulse amplitude and phase against velocity deceptive jamming. In particular, a design criterion incorporating a weighted sum of jamming energy and target sidelobe energy in the stopband is developed to minimize under phase quantized and peak-to-average power ratio (PAR) restriction. To tackle the resulting non-convex optimization problem, a new inexact alternating direction penalty method (IADPM) is proposed based on the ADPM framework and the computational complexity is analyzed. Finally, numerical results are provided to demonstrate the effectiveness of the proposed methodology.
2021, 50(4): 488-495.
doi: 10.12178/1001-0548.2021038
Abstract:
This paper proposes a routing algorithm based on fish swarm optimization for vehicular Ad Hoc network (VANET) in urban road environment. In vehicular Ad Hoc networks, the high-speed movement of vehicles leads to frequent changes of network conditions and topology. This paper uses fish swarm optimization model to assist the search and find a new optimal relay node for vehicles to ensure the message transmission in VANET. This paper proposes the fish swarm routing (FSR) optimization algorithm, which has the advantages of fast convergence and high efficiency. Experimental results show that the proposed algorithm has lower transmission delay and higher packet delivery ratio compared to existing approaches.
This paper proposes a routing algorithm based on fish swarm optimization for vehicular Ad Hoc network (VANET) in urban road environment. In vehicular Ad Hoc networks, the high-speed movement of vehicles leads to frequent changes of network conditions and topology. This paper uses fish swarm optimization model to assist the search and find a new optimal relay node for vehicles to ensure the message transmission in VANET. This paper proposes the fish swarm routing (FSR) optimization algorithm, which has the advantages of fast convergence and high efficiency. Experimental results show that the proposed algorithm has lower transmission delay and higher packet delivery ratio compared to existing approaches.
2021, 50(4): 496-501.
doi: 10.12178/1001-0548.2020280
Abstract:
In image communication, in order to save network resources and ensure reliable transmission in multi hop network, an image transmission scheme based on sliding window batched sparse (BATS) code and wavelet set partitioning in hierarchical trees (SPIHT) coding is proposed. Compared with the image transmission scheme based on traditional BATS code and SPIHT code, and image transmission scheme based on common sliding window BATS code and SPIHT code, this scheme can not only improve the influence of coding randomness of BATS code on transmission reliability, but also greatly reduce the transmission delay while ensuring the progressive transmission characteristics of the scheme, so as to improve the quality of experience (QoE) on the image communication system.
In image communication, in order to save network resources and ensure reliable transmission in multi hop network, an image transmission scheme based on sliding window batched sparse (BATS) code and wavelet set partitioning in hierarchical trees (SPIHT) coding is proposed. Compared with the image transmission scheme based on traditional BATS code and SPIHT code, and image transmission scheme based on common sliding window BATS code and SPIHT code, this scheme can not only improve the influence of coding randomness of BATS code on transmission reliability, but also greatly reduce the transmission delay while ensuring the progressive transmission characteristics of the scheme, so as to improve the quality of experience (QoE) on the image communication system.
2021, 50(4): 502-507.
doi: 10.12178/1001-0548.2020417
Abstract:
Dynamic thermal management is used to handle the thermal problem of very large scale integrated circuits (VLSI), such us multicore processors. Accurate monitoring of the temperature field can insure dynamic thermal management working correctly, guarantee the chip working performance and reliability further. The temperature field reconstruction techniques based on analysis in frequency domain ignore the information in high frequency zone, which leads to thermal field recovery inaccurate. In order to improve the precision of thermal field reconstruction, a new thermal field reconstruction method based on sparse dictionary learning technology is proposed. In this method, the prior information of temperature field is sparse represented by dictionary learning, and the location assignment scheme of temperature sensor is designed to realize the reconstruction of temperature field. The experiments prove that the proposed strategy have better performance than the methods based on analysis in frequency domain.
Dynamic thermal management is used to handle the thermal problem of very large scale integrated circuits (VLSI), such us multicore processors. Accurate monitoring of the temperature field can insure dynamic thermal management working correctly, guarantee the chip working performance and reliability further. The temperature field reconstruction techniques based on analysis in frequency domain ignore the information in high frequency zone, which leads to thermal field recovery inaccurate. In order to improve the precision of thermal field reconstruction, a new thermal field reconstruction method based on sparse dictionary learning technology is proposed. In this method, the prior information of temperature field is sparse represented by dictionary learning, and the location assignment scheme of temperature sensor is designed to realize the reconstruction of temperature field. The experiments prove that the proposed strategy have better performance than the methods based on analysis in frequency domain.
2021, 50(4): 508-513, 550.
doi: 10.12178/1001-0548.2021023
Abstract:
A resistive random access memory (RRAM)-based hybrid physical unclonable function (PUF) for chip fingerprint is described in this paper. The “E” shape central symmetrical RRAM uses graphene thin film as electrode layer, and has wide distribution of resistance and high on-off resistance ratio; the resistance variation of RRAM is introduced to PUF cell as an entropy source to improve the uniqueness of PUFs; the different resistance states of RRAM are used to amplify initial deviation of PUF cell and improve the stability of PUFs; the cycle-to-cycle variation of RRAM is utilized to reconstruct the chip ID and improve the security of PUFs. The proposed PUF is designed in a 0.35μm CMOS technology. Simulation results show that the proposed PUF has good characteristics of uniqueness and stability, the inter-chip hamming distance (HD) in normal conditions is 49.95%, and the bit error rate is zero when temperature varies from −40 ℃ to 100 ℃, and supply voltage changes from 4.6 V to 5.4 V.
A resistive random access memory (RRAM)-based hybrid physical unclonable function (PUF) for chip fingerprint is described in this paper. The “E” shape central symmetrical RRAM uses graphene thin film as electrode layer, and has wide distribution of resistance and high on-off resistance ratio; the resistance variation of RRAM is introduced to PUF cell as an entropy source to improve the uniqueness of PUFs; the different resistance states of RRAM are used to amplify initial deviation of PUF cell and improve the stability of PUFs; the cycle-to-cycle variation of RRAM is utilized to reconstruct the chip ID and improve the security of PUFs. The proposed PUF is designed in a 0.35μm CMOS technology. Simulation results show that the proposed PUF has good characteristics of uniqueness and stability, the inter-chip hamming distance (HD) in normal conditions is 49.95%, and the bit error rate is zero when temperature varies from −40 ℃ to 100 ℃, and supply voltage changes from 4.6 V to 5.4 V.
2021, 50(4): 514-519.
doi: 10.12178/1001-0548.2020328
Abstract:
By using the orbit-limited motion (OLM) method and the charging theory of dust particles, the charging effects of dust particles during the condition when dusty plasmas in mesosphere affected by high power radio wave are analyzed in this paper. In addition, the theoretical results are demonstrated by simulated experiments. Through theoretical analysis, it can be known that the number ofcharge dust particles is determined by the size of the dust radius, electron temperature, ion temperature, ion density and electron density. Based on the data of rocket-borne sensors and simulation results, it is found that the average number of charged dust decreases with an increase in dust charges number density and increases with an increase in dust radius, electron temperature, and electron density. The simulation results are close to the theoretical and experimental studied results, and the average radius are consistent with the results of reference when the average dust charge is 0.4e.
By using the orbit-limited motion (OLM) method and the charging theory of dust particles, the charging effects of dust particles during the condition when dusty plasmas in mesosphere affected by high power radio wave are analyzed in this paper. In addition, the theoretical results are demonstrated by simulated experiments. Through theoretical analysis, it can be known that the number ofcharge dust particles is determined by the size of the dust radius, electron temperature, ion temperature, ion density and electron density. Based on the data of rocket-borne sensors and simulation results, it is found that the average number of charged dust decreases with an increase in dust charges number density and increases with an increase in dust radius, electron temperature, and electron density. The simulation results are close to the theoretical and experimental studied results, and the average radius are consistent with the results of reference when the average dust charge is 0.4e.
2021, 50(4): 520-526.
doi: 10.12178/1001-0548.2021084
Abstract:
In this paper, a novel 10 kV SiC MOSFET embedding low barrier diode (LBD-MOSFET) is proposed and researched to solve the bipolar degradation effect in SiC MOSFET. The low barrier diode (LBD) in the cell is formed by introducing an N_well above the P_base region on one side, which reduces the electron barrier between the drain and the source. When the LBD-MOSFET works in the third quadrant, the low electronic barrier makes the LBD turn on with a lower source-drain voltage, thus effectively avoiding the bipolar degradation effect caused by the turn-on of the body diode. 2D numerical analysis results show that the breakdown voltage of the SiC LBD-MOSFET reaches 13.5 kV. In the third quadrant, the turn-on voltage is only 1.3 V, which is 48% lower than the traditional structure and effectively reduces the conduction loss of the device. At the same time, since the gate-drain overlap area of the LBD-MOSFET is reduced compared to the traditional MOSFET, the Cgd is only 1.0 pF/cm2 and the high-frequency merit value of the device is 194 mΩ·pF, which are reduced by 81% and 76% compared with the traditional MOSFET, respectively. Therefore, the LBD-MOSFET is suitable for high-frequency and high-reliability power electronic systems.
In this paper, a novel 10 kV SiC MOSFET embedding low barrier diode (LBD-MOSFET) is proposed and researched to solve the bipolar degradation effect in SiC MOSFET. The low barrier diode (LBD) in the cell is formed by introducing an N_well above the P_base region on one side, which reduces the electron barrier between the drain and the source. When the LBD-MOSFET works in the third quadrant, the low electronic barrier makes the LBD turn on with a lower source-drain voltage, thus effectively avoiding the bipolar degradation effect caused by the turn-on of the body diode. 2D numerical analysis results show that the breakdown voltage of the SiC LBD-MOSFET reaches 13.5 kV. In the third quadrant, the turn-on voltage is only 1.3 V, which is 48% lower than the traditional structure and effectively reduces the conduction loss of the device. At the same time, since the gate-drain overlap area of the LBD-MOSFET is reduced compared to the traditional MOSFET, the Cgd is only 1.0 pF/cm2 and the high-frequency merit value of the device is 194 mΩ·pF, which are reduced by 81% and 76% compared with the traditional MOSFET, respectively. Therefore, the LBD-MOSFET is suitable for high-frequency and high-reliability power electronic systems.
2021, 50(4): 527-534.
doi: 10.12178/1001-0548.2021068
Abstract:
For the rigid spacecraft attitude tracking control problem with parameter uncertainties and external disturbances, a method of finite time adaptive integral sliding mode controller is proposed in this paper. A spacecraft attitude tracking model is described with quaternion. The basic principles of finite time method are introduced. Then, an integral sliding surface is designed with finite time method, which estimates bound of the disturbances and parameter uncertainties. The method has the characteristic of integral sliding mode method and finite time method. Simulation results show the fine performance of the controller.
For the rigid spacecraft attitude tracking control problem with parameter uncertainties and external disturbances, a method of finite time adaptive integral sliding mode controller is proposed in this paper. A spacecraft attitude tracking model is described with quaternion. The basic principles of finite time method are introduced. Then, an integral sliding surface is designed with finite time method, which estimates bound of the disturbances and parameter uncertainties. The method has the characteristic of integral sliding mode method and finite time method. Simulation results show the fine performance of the controller.
2021, 50(4): 535-543.
doi: 10.12178/1001-0548.2020449
Abstract:
We propose a two-parameter generalized exponential distribution with closed-form expression based on the maximum Rényi entropy principle under the normalization and mean constraints, which is referred as the q-exponential distribution. The statistical properties of this distribution are investigated. The maximum likelihood method and information likelihood method are used to estimate the parameters of the proposed distribution, respectively. Two well-known data sets are employed to evaluate the q-exponential distribution, and the experimental results demonstrate that the proposed distribution can fit the data sets better than other well-known distributions, such as Weibull distribution and linear failure rate distribution. Additionally, the experiment results of life estimation of the Li-ion batteries prove that compared with the exponential distribution, the proposed distribution can give more accurate prediction. In the last experiment, the estimation accuracy is improved by at least 17.857%.
We propose a two-parameter generalized exponential distribution with closed-form expression based on the maximum Rényi entropy principle under the normalization and mean constraints, which is referred as the q-exponential distribution. The statistical properties of this distribution are investigated. The maximum likelihood method and information likelihood method are used to estimate the parameters of the proposed distribution, respectively. Two well-known data sets are employed to evaluate the q-exponential distribution, and the experimental results demonstrate that the proposed distribution can fit the data sets better than other well-known distributions, such as Weibull distribution and linear failure rate distribution. Additionally, the experiment results of life estimation of the Li-ion batteries prove that compared with the exponential distribution, the proposed distribution can give more accurate prediction. In the last experiment, the estimation accuracy is improved by at least 17.857%.
2021, 50(4): 544-550.
doi: 10.12178/1001-0548.2020446
Abstract:
In view of the problems that the existing traffic virtual simulation systems have, such as they can neither simultaneously support real-time online simulation for multiple users, nor meet the practical requirements of virtual simulation teaching experiment and even be difficult to expand on a large scale. In this paper, from the aspects of system architecture and simulation engine, a multi-user-oriented micro-traffic simulation experiment system is for the first time investigated and devised. Furthermore, this paper tests the designed system from three performance indicators including the vehicle number change of single simulation engine simulation, the acceleration ratio change of single simulation engine simulation, and the concurrent execution of multiple simulation engines. The experimental results indicate that the traffic virtual simulation system devised in this paper can support the concurrent execution of multiple simulation engines and multiple simulation tasks as well as meet the simultaneous online simulation needs of multiple users.
In view of the problems that the existing traffic virtual simulation systems have, such as they can neither simultaneously support real-time online simulation for multiple users, nor meet the practical requirements of virtual simulation teaching experiment and even be difficult to expand on a large scale. In this paper, from the aspects of system architecture and simulation engine, a multi-user-oriented micro-traffic simulation experiment system is for the first time investigated and devised. Furthermore, this paper tests the designed system from three performance indicators including the vehicle number change of single simulation engine simulation, the acceleration ratio change of single simulation engine simulation, and the concurrent execution of multiple simulation engines. The experimental results indicate that the traffic virtual simulation system devised in this paper can support the concurrent execution of multiple simulation engines and multiple simulation tasks as well as meet the simultaneous online simulation needs of multiple users.
2021, 50(4): 551-557.
doi: 10.12178/1001-0548.2021095
Abstract:
Encoder-decoder-based generative approaches have been widely used and achieved good performance for keyphrase extraction tasks. However, the main challenges of the encoder-decoder-based approach are modeling an effective document vector representation and generating a set of keyphrases covering the entire document topic, which can directly affect the keyphrase extraction results. In this paper, a document-level keyphrase extraction model incorporating neighborhood knowledge is proposed to address the challenges mentioned above simultaneously. Specifically, the original document is extending to a document set by adding some nearest-neighbor documents. Then, each document in the set is constructed into a word graph based on the distance between words, and all the word graphs in the set are merged into a large graph, which is then encoded using graph convolutional networks. Besides, to fully cover diverse keyphrases and topics, the context modification mechanism and coverage mechanism are introduced at the decoding step. Finally, by comparing with the existing baseline model on four benchmark datasets, the experimental results show that the method can effectively improve the performance of extracting keyphrases.
Encoder-decoder-based generative approaches have been widely used and achieved good performance for keyphrase extraction tasks. However, the main challenges of the encoder-decoder-based approach are modeling an effective document vector representation and generating a set of keyphrases covering the entire document topic, which can directly affect the keyphrase extraction results. In this paper, a document-level keyphrase extraction model incorporating neighborhood knowledge is proposed to address the challenges mentioned above simultaneously. Specifically, the original document is extending to a document set by adding some nearest-neighbor documents. Then, each document in the set is constructed into a word graph based on the distance between words, and all the word graphs in the set are merged into a large graph, which is then encoded using graph convolutional networks. Besides, to fully cover diverse keyphrases and topics, the context modification mechanism and coverage mechanism are introduced at the decoding step. Finally, by comparing with the existing baseline model on four benchmark datasets, the experimental results show that the method can effectively improve the performance of extracting keyphrases.
2021, 50(4): 558-564.
doi: 10.12178/1001-0548.2021058
Abstract:
Ideal adversarial examples should not only successfully deceive the machine learning classifier, but also should not easily be perceived by human vision. In the traditional algorithms, only the norm is adopted as a measurement index of the perturbation size of adversarial examples, which usually leads to the difference in the visibility range. In this paper, a method for adversarial examples generation based on the texture and perceptual color distance is developed. The main idea is to embed the perturbation into a high texture area of an image and optimize the perceptual color distance, so as to reduce the difference in the visibility range between the original image and adversarial example. Moreover, an automatic hyperparameter optimization method is employed to accelerate the convergence of backpropagation. Experimental evaluation shows that the proposed algorithm can obtain the smallest L2 norm and perceptual color distance than other algorithms. Meanwhile, a smaller number of iterations was required to obtain adversarial examples
Ideal adversarial examples should not only successfully deceive the machine learning classifier, but also should not easily be perceived by human vision. In the traditional algorithms, only the norm is adopted as a measurement index of the perturbation size of adversarial examples, which usually leads to the difference in the visibility range. In this paper, a method for adversarial examples generation based on the texture and perceptual color distance is developed. The main idea is to embed the perturbation into a high texture area of an image and optimize the perceptual color distance, so as to reduce the difference in the visibility range between the original image and adversarial example. Moreover, an automatic hyperparameter optimization method is employed to accelerate the convergence of backpropagation. Experimental evaluation shows that the proposed algorithm can obtain the smallest L2 norm and perceptual color distance than other algorithms. Meanwhile, a smaller number of iterations was required to obtain adversarial examples
2021, 50(4): 565-571.
doi: 10.12178/1001-0548.2019175
Abstract:
In order to solve the problems of key waste and low transmission efficiency in the existing routing scheme of quantum key distribution (QKD) network based on trust relay, a random routing algorithm based on backtracking is proposed to improve the existing routing algorithm. In the process of routing, the algorithm adds backtracking points to each branch, in the transmission process along the selected path, when the key quantity of a certain link is insufficient, by looking for the nearest backtracking point, the key can be transferred along the randomly selected new path again from the backtracking point. The comparison experiment and analysis results show that the algorithm has certain advantages in routing time, key consumption and key transmission efficiency.
In order to solve the problems of key waste and low transmission efficiency in the existing routing scheme of quantum key distribution (QKD) network based on trust relay, a random routing algorithm based on backtracking is proposed to improve the existing routing algorithm. In the process of routing, the algorithm adds backtracking points to each branch, in the transmission process along the selected path, when the key quantity of a certain link is insufficient, by looking for the nearest backtracking point, the key can be transferred along the randomly selected new path again from the backtracking point. The comparison experiment and analysis results show that the algorithm has certain advantages in routing time, key consumption and key transmission efficiency.
2021, 50(4): 572-579.
doi: 10.12178/1001-0548.2020228
Abstract:
This paper proposes a new scheduling algorithm which can reduce the unnecessary migration and context switching overhead. In this method, each real-time cycle task is proportionally executed on different processor cores, and the priority of task scheduling is specified, and then the real-time cycle tasks are scheduled according to the corresponding real-time scheduling process. By comparing with EDF-os and EDF-fm, which have been considered as set scheduling algorithms of high utilization rate, the experiments show this method not only can ensure high utilization rate but also reduce the times of migrating tasks and context switching overhead.
This paper proposes a new scheduling algorithm which can reduce the unnecessary migration and context switching overhead. In this method, each real-time cycle task is proportionally executed on different processor cores, and the priority of task scheduling is specified, and then the real-time cycle tasks are scheduled according to the corresponding real-time scheduling process. By comparing with EDF-os and EDF-fm, which have been considered as set scheduling algorithms of high utilization rate, the experiments show this method not only can ensure high utilization rate but also reduce the times of migrating tasks and context switching overhead.
2021, 50(4): 580-585.
doi: 10.12178/1001-0548.2020365
Abstract:
In order to accurately predict the changing trend of sensor data when the aeroengine is operating, and to effectively monitor the working status of the aeroengine, the sliding window algorithm is used to intercept the subsequences to construct the time series data set and standardize them based on the data of several main aeroengine sensors: engine high pressure compressor rotor speed (N2), combustion chamber fuel nozzle pressure (Ptk), turbine temperature (Tt6) and so on. Then we propose a multi-sensor data prediction model of aeroengine based on Seq2Seq which is called AMSDPNN (aeroengine multi-sensor data prediction neural network) and optimizes this neural network model to realize the prediction of aeroengine multi-sensor data. The experimental results show that this model has better prediction results than other traditional data prediction models and the mean square error (MSE) is 0.1%. And the prediction of aeroengine sensor data is advanced by 320ms.
In order to accurately predict the changing trend of sensor data when the aeroengine is operating, and to effectively monitor the working status of the aeroengine, the sliding window algorithm is used to intercept the subsequences to construct the time series data set and standardize them based on the data of several main aeroengine sensors: engine high pressure compressor rotor speed (N2), combustion chamber fuel nozzle pressure (Ptk), turbine temperature (Tt6) and so on. Then we propose a multi-sensor data prediction model of aeroengine based on Seq2Seq which is called AMSDPNN (aeroengine multi-sensor data prediction neural network) and optimizes this neural network model to realize the prediction of aeroengine multi-sensor data. The experimental results show that this model has better prediction results than other traditional data prediction models and the mean square error (MSE) is 0.1%. And the prediction of aeroengine sensor data is advanced by 320ms.
2021, 50(4): 586-591.
doi: 10.12178/1001-0548.2020272
Abstract:
When the ambient light of the robot changes, the performance of the loop closure detection algorithm based on the traditional visual word bag will decrease, and it is prone to perceptual aliasing and perceptual variation, thus judging the false closed-loop. In this paper, the original color image is used to generate an illumination invariant image related only to the light source, and then a visual dictionary of the original illumination invariant image is generated. For each image, two histograms and similarity scores are calculated to determine whether it is a closed loop. Finally, it is tested on the data set. The experimental results show that compared with the bag-of-words (BoW), the loop closure detection algorithm proposed in this paper has better robustness to the changes in the environment.
When the ambient light of the robot changes, the performance of the loop closure detection algorithm based on the traditional visual word bag will decrease, and it is prone to perceptual aliasing and perceptual variation, thus judging the false closed-loop. In this paper, the original color image is used to generate an illumination invariant image related only to the light source, and then a visual dictionary of the original illumination invariant image is generated. For each image, two histograms and similarity scores are calculated to determine whether it is a closed loop. Finally, it is tested on the data set. The experimental results show that compared with the bag-of-words (BoW), the loop closure detection algorithm proposed in this paper has better robustness to the changes in the environment.
2021, 50(4): 592-597.
doi: 10.12178/1001-0548.2021081
Abstract:
Based on the analysis of the principle of polarization independent modulation, a new type of inverted ridge polarization independent electro-optic modulator is proposed, which is inclined to place the graphene sheet in a silicon waveguide at a certain angle. At the wavelength of 1.55 μm, the effective mode parameters of TE and TM modes are the same, and the difference of absorption parameters is very small. In addition, the device can achieve extinction ratio higher than 18 dB for TE and TM modes at wavelengths from 1.5 μm to 1.6 μm, and the difference of extinction ratio is less than 4 dB. The 3 dB modulation bandwidth of the device can reach 123 GHz in ideal state.
Based on the analysis of the principle of polarization independent modulation, a new type of inverted ridge polarization independent electro-optic modulator is proposed, which is inclined to place the graphene sheet in a silicon waveguide at a certain angle. At the wavelength of 1.55 μm, the effective mode parameters of TE and TM modes are the same, and the difference of absorption parameters is very small. In addition, the device can achieve extinction ratio higher than 18 dB for TE and TM modes at wavelengths from 1.5 μm to 1.6 μm, and the difference of extinction ratio is less than 4 dB. The 3 dB modulation bandwidth of the device can reach 123 GHz in ideal state.
2021, 50(4): 598-602.
doi: 10.12178/1001-0548.2021034
Abstract:
An ultra-fast and high-resolution optical vector analysis scheme has been proposed and experimentally demonstrated. In the scheme, a broadband optical frequency-sweeping signal is generated by injecting a linearly frequency-modulated signal into one radio-frequency (RF) port of a dual-drive Mach-Zehnder electro-optic modulator, which is used to achieve fast scanning of the frequency response characteristic of the device under test (DUT). A single-tone microwave signal is injected into the other RF port of the modulator to realize down-conversion of the frequency-sweeping signal. The down-converted signal is then digitized by an analog-to-digital converter, and is processed through Hilbert transform to extract the amplitude- and phase-frequency response of the DUT. In the proof-of-concept experiment, the amplitude- and phase-frequency response of the Brillouin gain in a section of non-zero dispersion shifted fiber with a length of 3 km is accurately measured, where the measurement time is only 20 μs, and the frequency resolution reaches 20 kHz.
An ultra-fast and high-resolution optical vector analysis scheme has been proposed and experimentally demonstrated. In the scheme, a broadband optical frequency-sweeping signal is generated by injecting a linearly frequency-modulated signal into one radio-frequency (RF) port of a dual-drive Mach-Zehnder electro-optic modulator, which is used to achieve fast scanning of the frequency response characteristic of the device under test (DUT). A single-tone microwave signal is injected into the other RF port of the modulator to realize down-conversion of the frequency-sweeping signal. The down-converted signal is then digitized by an analog-to-digital converter, and is processed through Hilbert transform to extract the amplitude- and phase-frequency response of the DUT. In the proof-of-concept experiment, the amplitude- and phase-frequency response of the Brillouin gain in a section of non-zero dispersion shifted fiber with a length of 3 km is accurately measured, where the measurement time is only 20 μs, and the frequency resolution reaches 20 kHz.
2021, 50(4): 603-608.
doi: 10.12178/1001-0548.2020416
Abstract:
Reliability analysis and fault diagnosis for dynamic systems have always been hot topics in this field. As one of the popular reliability analysis methods, dynamic bayesian network (DBN) has been fully studied. However, the existing DBN algorithm has no general inference engines, and the modeling difficulty increases exponentially with the system complexity. This paper proposes a general probability table modeling method, which can also be applied on the dynamic reliability analysis of the system under the continuous mission time. Additionally, via the Bayesian inference algorithm, the posterior probability of component failure can be obtained, which can also be applied on system fault diagnosis. Finally, the validation of proposed method is verified by the reliability analysis and fault diagnosis of the power system.
Reliability analysis and fault diagnosis for dynamic systems have always been hot topics in this field. As one of the popular reliability analysis methods, dynamic bayesian network (DBN) has been fully studied. However, the existing DBN algorithm has no general inference engines, and the modeling difficulty increases exponentially with the system complexity. This paper proposes a general probability table modeling method, which can also be applied on the dynamic reliability analysis of the system under the continuous mission time. Additionally, via the Bayesian inference algorithm, the posterior probability of component failure can be obtained, which can also be applied on system fault diagnosis. Finally, the validation of proposed method is verified by the reliability analysis and fault diagnosis of the power system.
2021, 50(4): 609-615.
doi: 10.12178/1001-0548.2021082
Abstract:
Biochemical detection and analysis plays an important role in many fields, and many of the target substances are in low concentrations. Microfluidic detection platform has been developed for its low cost and rapid detection. Microfluidic enrichment is important to improve the sensitivity of microfluidic detection system. This paper studied the enrichment of fluorescent ions and cortisol aptamers using a double-T-shape channel, which could achieve the function of sample driving by electrokinetics. It is found that the structure has different enrichment effect and different enrichment area for different sizes of substances. For those substances with larger size relative to ions, it can be concentrated in a short time under low voltages, and the enrichment region is far away from the microelectrode, which is of benefit to both the sample and the electrode. By using this new enrichment effect, we achieved the detection of cortisol with the concentration of 0.1 μg/mL.
Biochemical detection and analysis plays an important role in many fields, and many of the target substances are in low concentrations. Microfluidic detection platform has been developed for its low cost and rapid detection. Microfluidic enrichment is important to improve the sensitivity of microfluidic detection system. This paper studied the enrichment of fluorescent ions and cortisol aptamers using a double-T-shape channel, which could achieve the function of sample driving by electrokinetics. It is found that the structure has different enrichment effect and different enrichment area for different sizes of substances. For those substances with larger size relative to ions, it can be concentrated in a short time under low voltages, and the enrichment region is far away from the microelectrode, which is of benefit to both the sample and the electrode. By using this new enrichment effect, we achieved the detection of cortisol with the concentration of 0.1 μg/mL.
2021, 50(4): 616-626.
doi: 10.12178/1001-0548.2020441
Abstract:
Baidu migration scale index represents the human migration scale of a specific area in China, and it has been used widely in geo-economics, demography, and epidemiology. Nowadays, Baidu migration index is adopted as a key data source for studying epidemic models of COVID-19. But the index is just a dimensionless number, its constitution method is still ambiguous. In this paper, the index is assumed as an elementary function mapping result of the real human migrate populations. According to a hidden equation existing in the data set, the mapping function is deduced to be a linear function y=kx. Another key phenomenon in the data set is the minimum interval of the migration index. All the migration index values and their differentials are exactly divisible by this interval. Through Fermat-Euler Theorem, we prove the coprimeness of the human migrate populations, and then the relationship between the minimum interval and minimum counting unit of the migrate populations is built, which means k=3.24×10−5. In the experiments, the migration records between 01/01/2020−04/30/2020 are examined to verify the correctness of the hidden equation: while the rounding error is considered, there about 93.81% of the city-to-city migration records, 82.65% city-to-province migration records and 84.87% province-to-province migration records can support the equation exactly; the maximum absolute error of the violation records is 357 peoples, which corresponds to about 0.5% relative error. The verifications support the self-consistency of the proposed linear mapping function.
Baidu migration scale index represents the human migration scale of a specific area in China, and it has been used widely in geo-economics, demography, and epidemiology. Nowadays, Baidu migration index is adopted as a key data source for studying epidemic models of COVID-19. But the index is just a dimensionless number, its constitution method is still ambiguous. In this paper, the index is assumed as an elementary function mapping result of the real human migrate populations. According to a hidden equation existing in the data set, the mapping function is deduced to be a linear function y=kx. Another key phenomenon in the data set is the minimum interval of the migration index. All the migration index values and their differentials are exactly divisible by this interval. Through Fermat-Euler Theorem, we prove the coprimeness of the human migrate populations, and then the relationship between the minimum interval and minimum counting unit of the migrate populations is built, which means k=3.24×10−5. In the experiments, the migration records between 01/01/2020−04/30/2020 are examined to verify the correctness of the hidden equation: while the rounding error is considered, there about 93.81% of the city-to-city migration records, 82.65% city-to-province migration records and 84.87% province-to-province migration records can support the equation exactly; the maximum absolute error of the violation records is 357 peoples, which corresponds to about 0.5% relative error. The verifications support the self-consistency of the proposed linear mapping function.
2021, 50(4): 627-633.
doi: 10.12178/1001-0548.2020440
Abstract:
The research on robustness indicators is mainly focused on single network, but less on interdependent networks. This paper explores some commonly used robustness indicators. Aiming at the cascading failure of interdependent networks, a new robustness indicator based on the largest-component relative efficiency is proposed. With the simulation for rationality verification, the results show that the indicator proposed in this paper is more accurate to measure the robustness variations of the interdependent networks in the cascading failure process than those are commonly used at present, and it can be applied to simulation-based robustness analysis for large-scale interdependent networks.
The research on robustness indicators is mainly focused on single network, but less on interdependent networks. This paper explores some commonly used robustness indicators. Aiming at the cascading failure of interdependent networks, a new robustness indicator based on the largest-component relative efficiency is proposed. With the simulation for rationality verification, the results show that the indicator proposed in this paper is more accurate to measure the robustness variations of the interdependent networks in the cascading failure process than those are commonly used at present, and it can be applied to simulation-based robustness analysis for large-scale interdependent networks.
2021, 50(4): 634-640.
doi: 10.12178/1001-0548.2021079
Abstract:
In the iterated prisoners’ dilemma game, zero-determinant strategies can unilaterally form a linear relationship between the payoffs of the players, where the extortion strategy always obtains a benefit no less than that of her opponent. We focus on the evolution of the cooperation defection and extortion strategies on the grid network when agents update their strategies by replication-aspiration dynamic. By means of Monte Carlo simulations, we find that the extortion strategy promotes the boost of the cooperation on the grid network under the mixed updating rule. We explain the results by the micro dynamic of the process and find that the existence of "cooperator - extortioner alliance" can help the cooperators resist the invasion of the defectors and the strength of the extortion strategies plays a non-trivial role on the evolution of cooperation.
In the iterated prisoners’ dilemma game, zero-determinant strategies can unilaterally form a linear relationship between the payoffs of the players, where the extortion strategy always obtains a benefit no less than that of her opponent. We focus on the evolution of the cooperation defection and extortion strategies on the grid network when agents update their strategies by replication-aspiration dynamic. By means of Monte Carlo simulations, we find that the extortion strategy promotes the boost of the cooperation on the grid network under the mixed updating rule. We explain the results by the micro dynamic of the process and find that the existence of "cooperator - extortioner alliance" can help the cooperators resist the invasion of the defectors and the strength of the extortion strategies plays a non-trivial role on the evolution of cooperation.
