ISSN 
2020 Vol. 49, No. 6
2020, 49(6): 801-805.
doi: 10.12178/1001-0548.2019166
Abstract:
Linear frequency modulated continuous wave (LFMCW) vehicle radars usually adopts multiple input multiple output (MIMO) virtual aperture angle measurement method. Traditional methods split velocity ambiguity resolution and angle estimation in two frames for further processing, causing the reduction of radar data rate. In this paper, a new method is proposed to solve this problem. This method uses the Time division multiplexing (TDM) approach to transmit and process sawtooth signals with different slope. Only one frame signal is needed for the velocity ambiguity resolution and angle estimation by using phase compensation. Simulation and theoretical analysis show that the proposed method can reduce signal processing time and improve radar data rate, with high angle measurement accuracy obtained simultaneously.
Linear frequency modulated continuous wave (LFMCW) vehicle radars usually adopts multiple input multiple output (MIMO) virtual aperture angle measurement method. Traditional methods split velocity ambiguity resolution and angle estimation in two frames for further processing, causing the reduction of radar data rate. In this paper, a new method is proposed to solve this problem. This method uses the Time division multiplexing (TDM) approach to transmit and process sawtooth signals with different slope. Only one frame signal is needed for the velocity ambiguity resolution and angle estimation by using phase compensation. Simulation and theoretical analysis show that the proposed method can reduce signal processing time and improve radar data rate, with high angle measurement accuracy obtained simultaneously.
2020, 49(6): 806-811.
doi: 10.12178/1001-0548.2020025
Abstract:
In order to better solve multi-objective problem and improve the diversity and convergence of multi-objective optimization algorithms, an improved multi-objective particle swarm optimization algorithm is proposed. The algorithm divides the population into several subpopulations for optimization search and improves the particle velocity updating formula to expend the coverage of Pareto optimal solution set, and use inverse trigonometric logistic mapping to initialize the population to make the distribution of initial population more uniform. The time-varying variation method is used to change the external files to avoid local optimization. By comparing the performance of improved algorithm, standard multi-objective particle swarm optimization (MOPSO) algorithms and NSGA-Ⅱ on the standard test function, the effectiveness of the improved algorithm proposed in this paper is verified in an optimal radar distribution station.
In order to better solve multi-objective problem and improve the diversity and convergence of multi-objective optimization algorithms, an improved multi-objective particle swarm optimization algorithm is proposed. The algorithm divides the population into several subpopulations for optimization search and improves the particle velocity updating formula to expend the coverage of Pareto optimal solution set, and use inverse trigonometric logistic mapping to initialize the population to make the distribution of initial population more uniform. The time-varying variation method is used to change the external files to avoid local optimization. By comparing the performance of improved algorithm, standard multi-objective particle swarm optimization (MOPSO) algorithms and NSGA-Ⅱ on the standard test function, the effectiveness of the improved algorithm proposed in this paper is verified in an optimal radar distribution station.
2020, 49(6): 812-817.
doi: 10.12178/1001-0548.2019192
Abstract:
Target track quality evaluation is a problem of multi-index evaluations, including the accuracy and continuity of the target state. The present evaluation indexes are not applicable to the real-time evaluation of the target track quality. Furthermore, in the comprehensive evaluation of track quality, weighted coefficients of each index are directly given by experience and thereby lack theoretical basis. To solve the problems above, a real-time evaluation method of target track quality based on location information is proposed. Firstly, a real-time evaluation index system of target track quality is established according to target location information, and the recursive calculation method of the evaluation index is given. Then, the weighted coefficients of evaluation index are determined based on the analytic hierarchy process. Finally, the method of weighted summation is used to evaluate the comprehensive quality of the target track. The experimental results based on simulation data proved the effectiveness of the proposed method.
Target track quality evaluation is a problem of multi-index evaluations, including the accuracy and continuity of the target state. The present evaluation indexes are not applicable to the real-time evaluation of the target track quality. Furthermore, in the comprehensive evaluation of track quality, weighted coefficients of each index are directly given by experience and thereby lack theoretical basis. To solve the problems above, a real-time evaluation method of target track quality based on location information is proposed. Firstly, a real-time evaluation index system of target track quality is established according to target location information, and the recursive calculation method of the evaluation index is given. Then, the weighted coefficients of evaluation index are determined based on the analytic hierarchy process. Finally, the method of weighted summation is used to evaluate the comprehensive quality of the target track. The experimental results based on simulation data proved the effectiveness of the proposed method.
2020, 49(6): 818-825.
doi: 10.12178/1001-0548.2019293
Abstract:
This paper focuses on the mean square consensus problem of multi-agent system which has the nonconvex input and Markov switching graphs. First, a non-convex constraint operator is introduced and a distributed control algorithm is designed according to the neighbor node information in the multi-agent system. Then, based on the property of non-negative matrix, the sufficient and necessary conditions are obtained for the mean square consensus problem of the multi-agent system with Markov switching graphs. Finally, numerical simulation results validate that the mean square consensus can be achieved when the control input is restricted in a nonconvex set.
This paper focuses on the mean square consensus problem of multi-agent system which has the nonconvex input and Markov switching graphs. First, a non-convex constraint operator is introduced and a distributed control algorithm is designed according to the neighbor node information in the multi-agent system. Then, based on the property of non-negative matrix, the sufficient and necessary conditions are obtained for the mean square consensus problem of the multi-agent system with Markov switching graphs. Finally, numerical simulation results validate that the mean square consensus can be achieved when the control input is restricted in a nonconvex set.
2020, 49(6): 826-836.
doi: 10.12178/1001-0548.2020356
Abstract:
Heterogeneous ultra-dense network (H-UDN) is envisioned as a promising solution to sustain the explosive mobile traffic demand through network densification. By placing access points, processors, and storage units as close as possible to mobile users, H-UDNs bring forth a number of advantages, including high spectral efficiency, high energy efficiency, and low latency. Nonetheless, the high density and diversity of network entities in H-UDNs introduce formidable design challenges in collaborative signal processing and resource management. This article illustrates the great potential of machine learning techniques in solving these challenges. In particular, we show how to utilize graphical representations of H-UDNs to design efficient machine learning algorithms.
Heterogeneous ultra-dense network (H-UDN) is envisioned as a promising solution to sustain the explosive mobile traffic demand through network densification. By placing access points, processors, and storage units as close as possible to mobile users, H-UDNs bring forth a number of advantages, including high spectral efficiency, high energy efficiency, and low latency. Nonetheless, the high density and diversity of network entities in H-UDNs introduce formidable design challenges in collaborative signal processing and resource management. This article illustrates the great potential of machine learning techniques in solving these challenges. In particular, we show how to utilize graphical representations of H-UDNs to design efficient machine learning algorithms.
2020, 49(6): 837-842.
doi: 10.12178/1001-0548.2019304
Abstract:
In the purpose of achieving a high precise energy measurement in in-beam PET system which being used in heavy-ion cancer therapy, an application specific integrated circuit (ASIC) chip has been designed. Each channel in this four-channel chip consist has a pole-zero cancelation circuit, a low-pass filter circuit and a buffer circuit. The ASIC was fabricated in a commercial 350-nm CMOS process with a footprint of 2.6 mm × 1.25 mm and dissipates 6 mW/Ch of static power in 3.3 V power supply. In four classes peaking time (50 ns、100 ns、1 us、2us), an input dynamic about −0.8~+1 V, an integral nonlinearity (INL) less than 0.12%, an energy resolution less than 0.3%, a crosstalk less than 0.32% and a gain error between 4 channels less than 1.01% have been achieved in laboratory test. A relative energy resolution result was obtained with a Na-22 source, a LaBr3 crystalloid and a PMT, which is precise than amplifier Ortec 572.
In the purpose of achieving a high precise energy measurement in in-beam PET system which being used in heavy-ion cancer therapy, an application specific integrated circuit (ASIC) chip has been designed. Each channel in this four-channel chip consist has a pole-zero cancelation circuit, a low-pass filter circuit and a buffer circuit. The ASIC was fabricated in a commercial 350-nm CMOS process with a footprint of 2.6 mm × 1.25 mm and dissipates 6 mW/Ch of static power in 3.3 V power supply. In four classes peaking time (50 ns、100 ns、1 us、2us), an input dynamic about −0.8~+1 V, an integral nonlinearity (INL) less than 0.12%, an energy resolution less than 0.3%, a crosstalk less than 0.32% and a gain error between 4 channels less than 1.01% have been achieved in laboratory test. A relative energy resolution result was obtained with a Na-22 source, a LaBr3 crystalloid and a PMT, which is precise than amplifier Ortec 572.
2020, 49(6): 843-847.
doi: 10.12178/1001-0548.2020007
Abstract:
Radar angel echo is usually caused by some special weather conditions, which will lead to multiple slow-moving “target” tracks on radar terminal, affecting radar performance and disturbing operator’s judgment. In this paper, based on acquisition and analysis of original radar signal containing angel echo, the characteristic differences between angel echo and real target are extracted, and by summarizing amplitude characteristics, Doppler characteristics, spatial distribution and motion law of angel echo, a multi-criteria decision-making clutter suppression method based on analytic hierarchy process (AHP) is proposed. The processing results of real experimental data show that radar angel echo can be accurately distinguished and well suppressed by adopting the method.
Radar angel echo is usually caused by some special weather conditions, which will lead to multiple slow-moving “target” tracks on radar terminal, affecting radar performance and disturbing operator’s judgment. In this paper, based on acquisition and analysis of original radar signal containing angel echo, the characteristic differences between angel echo and real target are extracted, and by summarizing amplitude characteristics, Doppler characteristics, spatial distribution and motion law of angel echo, a multi-criteria decision-making clutter suppression method based on analytic hierarchy process (AHP) is proposed. The processing results of real experimental data show that radar angel echo can be accurately distinguished and well suppressed by adopting the method.
2020, 49(6): 848-853.
doi: 10.12178/1001-0548.2020026
Abstract:
Because there are more than 20 performance parameters of space travelling wave tube (TWT), and they restrict each other, so the traditional manual debugging has great limitations, which is one of the reasons that restrict the performance improvement of TWT. A multi-objective optimization system of space TWT based on the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) is introduced in this paper. This system is composed of multi-objective optimization algorithm, automatic test system for electrical parameters of TWT, program-controlled high-voltage power supply and integrated test platform, which has the advantages of high automation and high reliability. It is not only helpful to form a standardized and standardized debugging process, but also of great significance to improve the potential performance of space TWT.
Because there are more than 20 performance parameters of space travelling wave tube (TWT), and they restrict each other, so the traditional manual debugging has great limitations, which is one of the reasons that restrict the performance improvement of TWT. A multi-objective optimization system of space TWT based on the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) is introduced in this paper. This system is composed of multi-objective optimization algorithm, automatic test system for electrical parameters of TWT, program-controlled high-voltage power supply and integrated test platform, which has the advantages of high automation and high reliability. It is not only helpful to form a standardized and standardized debugging process, but also of great significance to improve the potential performance of space TWT.
2020, 49(6): 854-859.
doi: 10.12178/1001-0548.2019301
Abstract:
To study the detection of dynamic link library (DLL) injected by static modifying import table of portable executable (PE) file, a common detection method on legal scope and a depth detection method on exception backtracking are proposed. The first method calculates the range of data structure arrangement of all DLLs from a static point of view, without parsing the DLL’s function to infer whether it is malicious. The idea of debugging is used to detect malicious DLLs in second method, which control the running of the target program, and track the DLL loading process in the initialization phase of the target program. Also the debugging API is used for exception capture to realize detection. C++ was used to design DLL detection experiment: injected the DLL with download function into the target program. The detection tool DLL Detector was designed and developed for detection. The experiment successfully detects suspicious modules from the static phase and the program initialization phase. Both methods support 32-bit and 64-bit PE files and can be used to guard against malicious code.
To study the detection of dynamic link library (DLL) injected by static modifying import table of portable executable (PE) file, a common detection method on legal scope and a depth detection method on exception backtracking are proposed. The first method calculates the range of data structure arrangement of all DLLs from a static point of view, without parsing the DLL’s function to infer whether it is malicious. The idea of debugging is used to detect malicious DLLs in second method, which control the running of the target program, and track the DLL loading process in the initialization phase of the target program. Also the debugging API is used for exception capture to realize detection. C++ was used to design DLL detection experiment: injected the DLL with download function into the target program. The detection tool DLL Detector was designed and developed for detection. The experiment successfully detects suspicious modules from the static phase and the program initialization phase. Both methods support 32-bit and 64-bit PE files and can be used to guard against malicious code.
2020, 49(6): 860-866.
doi: 10.12178/1001-0548.2020202
Abstract:
Outlier detection as one of the hot issues in data mining area aims to discover the objects with abnormal behaviors from the original data distribution. And it can generate many valuable applications, e.g., bank fraud, network instruction and etc. Currently, distributed computing has been widely applied in outlier detection. However, it still brings the lower performance of data computing since there are computing differences in compute nodes of distributed environment. To solve the problem of load balancing in distributed computing-based outlier detection with respect to large scale and high dimensional data, a weighted distributed outlier detection method has been proposed. First, we tend to ascertain the weight of data node based on computing performance of data node, whereafter dividing the data space into several grids. At last, for the purpose of parallel computing, a weighted grid-based allocation algorithm based on grid dividing is proposed, which allocates the grids to configured data nodes. The extensive experiments verify the effectiveness of proposed method, and demonstrate its better performance.
Outlier detection as one of the hot issues in data mining area aims to discover the objects with abnormal behaviors from the original data distribution. And it can generate many valuable applications, e.g., bank fraud, network instruction and etc. Currently, distributed computing has been widely applied in outlier detection. However, it still brings the lower performance of data computing since there are computing differences in compute nodes of distributed environment. To solve the problem of load balancing in distributed computing-based outlier detection with respect to large scale and high dimensional data, a weighted distributed outlier detection method has been proposed. First, we tend to ascertain the weight of data node based on computing performance of data node, whereafter dividing the data space into several grids. At last, for the purpose of parallel computing, a weighted grid-based allocation algorithm based on grid dividing is proposed, which allocates the grids to configured data nodes. The extensive experiments verify the effectiveness of proposed method, and demonstrate its better performance.
2020, 49(6): 867-874.
doi: 10.12178/1001-0548.2019228
Abstract:
The existing image caption methods have some problems that the caption sentences are not rich and accurate, and the model structures are complicated and difficult to train. We propose a novel end-to-end image caption method called image caption based on multimodal attention mechanism (M-AT). Firstly, it takes the keyword image feature extraction model (K-IFE) to extract better spatial features and keyword features, uses the keyword attention mechanism model (K-AT) to focus on important description words, and applies the spatial attention mechanism model (S-AT) to pay attention to more important areas of the image and simplify the model structure. The two attention mechanisms, K-AT and S-AT, can correct each other. The proposed method can generate more accurate and rich image description sentences. The experimental results on the MSCOCO data set show that the proposed method is effective, has around 2% improvement in some evaluation indicators.
The existing image caption methods have some problems that the caption sentences are not rich and accurate, and the model structures are complicated and difficult to train. We propose a novel end-to-end image caption method called image caption based on multimodal attention mechanism (M-AT). Firstly, it takes the keyword image feature extraction model (K-IFE) to extract better spatial features and keyword features, uses the keyword attention mechanism model (K-AT) to focus on important description words, and applies the spatial attention mechanism model (S-AT) to pay attention to more important areas of the image and simplify the model structure. The two attention mechanisms, K-AT and S-AT, can correct each other. The proposed method can generate more accurate and rich image description sentences. The experimental results on the MSCOCO data set show that the proposed method is effective, has around 2% improvement in some evaluation indicators.
2020, 49(6): 875-882.
doi: 10.12178/1001-0548.2020153
Abstract:
A differential evolutionary (DE) algorithm based on search preference knowledge — PKLSHADE (i.e. preference-knowledge-based LSHADE) is proposed for complex multimodal optimization. PKLSHADE applies prior search preference knowledge in the evolutionary process of the population and differentiates the diversity and convergence of the population at different evolutionary stages, i.e., the importance is attached to the perturbation in the early stages of evolution to enhance the global development of the algorithm, and more local searches centering around the current optimal solution are carried out in the later stage. At the same time, the mutation strategy based on search preference knowledge can realize the global development of differential evolutionary algorithm and the smooth adaptive transition of local search to avoid the direct switch of the two search stages. The experimental results on the complex multimodal functions of CEC2017 show that PKLSHADE is superior to recent excellent algorithms such as LSHADE, EBLSHADE, jSO and AMECoDEs in terms of the accuracy of the optimal solution and the stability of the algorithm.
A differential evolutionary (DE) algorithm based on search preference knowledge — PKLSHADE (i.e. preference-knowledge-based LSHADE) is proposed for complex multimodal optimization. PKLSHADE applies prior search preference knowledge in the evolutionary process of the population and differentiates the diversity and convergence of the population at different evolutionary stages, i.e., the importance is attached to the perturbation in the early stages of evolution to enhance the global development of the algorithm, and more local searches centering around the current optimal solution are carried out in the later stage. At the same time, the mutation strategy based on search preference knowledge can realize the global development of differential evolutionary algorithm and the smooth adaptive transition of local search to avoid the direct switch of the two search stages. The experimental results on the complex multimodal functions of CEC2017 show that PKLSHADE is superior to recent excellent algorithms such as LSHADE, EBLSHADE, jSO and AMECoDEs in terms of the accuracy of the optimal solution and the stability of the algorithm.
2020, 49(6): 883-890.
doi: 10.12178/1001-0548.2020132
Abstract:
The entity relation extraction is the key part to estimate the novelty of patents. The traditional entity relation extraction is the series system, but this style has major dwawbacks. The paper studies the evolution of entity relation extraction using two improved BERT algorithms. One is the method combining traditional Chinese features with syntactic semantic features, and the other is the method combining attention mechanism with syntactic semantic features. The extensive computational experiments and the preparation technology of the graphene show that the two algorithms can improve the analysis efficiency for the contents of the patents and reveal the dynamic evolution process of the technology of the graphene firm.
The entity relation extraction is the key part to estimate the novelty of patents. The traditional entity relation extraction is the series system, but this style has major dwawbacks. The paper studies the evolution of entity relation extraction using two improved BERT algorithms. One is the method combining traditional Chinese features with syntactic semantic features, and the other is the method combining attention mechanism with syntactic semantic features. The extensive computational experiments and the preparation technology of the graphene show that the two algorithms can improve the analysis efficiency for the contents of the patents and reveal the dynamic evolution process of the technology of the graphene firm.
2020, 49(6): 891-904.
doi: 10.12178/1001-0548.2020229
Abstract:
Both academia and government have shown an increasingly common interest in the relationship between the high-speed railway and socioeconomic development. This paper provides a systematic review of the recent theoretical and empirical literature on the impact of the high-speed railway on economic development. On the one hand, we summarize theoretical mechanisms that underline how high-speed rail affects economic development and review studies that explored the influence of high-speed rail on accessibility, population mobility and agglomeration, labor employment, and productivity. In particular, we focus on how high-speed rail affects the structure of the spatial economy. On the other hand, we summarize the effect of high-speed rail on industry and regional economic growth, based on which we further discuss the possible negative effects of introducing high-speed rail and provide some potential policy recommendations. At last, we suggest some promising research questions for future studies.
Both academia and government have shown an increasingly common interest in the relationship between the high-speed railway and socioeconomic development. This paper provides a systematic review of the recent theoretical and empirical literature on the impact of the high-speed railway on economic development. On the one hand, we summarize theoretical mechanisms that underline how high-speed rail affects economic development and review studies that explored the influence of high-speed rail on accessibility, population mobility and agglomeration, labor employment, and productivity. In particular, we focus on how high-speed rail affects the structure of the spatial economy. On the other hand, we summarize the effect of high-speed rail on industry and regional economic growth, based on which we further discuss the possible negative effects of introducing high-speed rail and provide some potential policy recommendations. At last, we suggest some promising research questions for future studies.
2020, 49(6): 905-917.
doi: 10.12178/1001-0548.2019182
Abstract:
Social network is a complex network with interaction characteristics. It can link nodes in different social networks by using the network characteristics of complex network, analyze the connections between nodes, and combine with the related matching algorithm to identify user’s virtual accounts, which can help social networks to provide users with better services. This paper presents a systematic review on across social networks user identification techniques proposed in the field of data mining. Then the methods for calculating the similarity of the three types of user identification techniques and the unified identification framework are elaborated in detail. The relevant evaluation metrics are used to evaluate the classified user identification technique performances. Finally, the future research directions of across social networks user identification techniques are prospected based on the analysis of the research status.
Social network is a complex network with interaction characteristics. It can link nodes in different social networks by using the network characteristics of complex network, analyze the connections between nodes, and combine with the related matching algorithm to identify user’s virtual accounts, which can help social networks to provide users with better services. This paper presents a systematic review on across social networks user identification techniques proposed in the field of data mining. Then the methods for calculating the similarity of the three types of user identification techniques and the unified identification framework are elaborated in detail. The relevant evaluation metrics are used to evaluate the classified user identification technique performances. Finally, the future research directions of across social networks user identification techniques are prospected based on the analysis of the research status.
2020, 49(6): 918-923.
doi: 10.12178/1001-0548.2018331
Abstract:
Credit allocation of each author of a multi-author paper has been a long standing concern. Regarding to the fact that the credit of each paper is not equal, this paper developes an improved credit allocation method, namely ACA_PR method. It uses the PageRank value and total citation of papers as total credit of one paper in order to measure the value of paper, and it constructs a co-citation network of collaborators’ scientific research records and cited research results, and distributes the contributions of the co-authors of the papers. By distinguishing the laureates of the Nobel Prize in Physics from the authors of prize-winning papers in American Physical Society (APS) dataset, this paper validates the ACA_PR method. Result shows that the ACA_PR method outperforms the state-of-the-art methods, and the accuracy of identifying the Nobel Prize laureates in Physics is 80.64% for 31 multi-author prize-winning papers in APS dataset. Accurate assessment the credit of researchers is significant in many aspects, such as hiring, funding and promotion, etc.
Credit allocation of each author of a multi-author paper has been a long standing concern. Regarding to the fact that the credit of each paper is not equal, this paper developes an improved credit allocation method, namely ACA_PR method. It uses the PageRank value and total citation of papers as total credit of one paper in order to measure the value of paper, and it constructs a co-citation network of collaborators’ scientific research records and cited research results, and distributes the contributions of the co-authors of the papers. By distinguishing the laureates of the Nobel Prize in Physics from the authors of prize-winning papers in American Physical Society (APS) dataset, this paper validates the ACA_PR method. Result shows that the ACA_PR method outperforms the state-of-the-art methods, and the accuracy of identifying the Nobel Prize laureates in Physics is 80.64% for 31 multi-author prize-winning papers in APS dataset. Accurate assessment the credit of researchers is significant in many aspects, such as hiring, funding and promotion, etc.
2020, 49(6): 924-933.
doi: 10.12178/1001-0548.2019234
Abstract:
This paper concerns the opinion evolution dynamics with malicious opinion injection in social networks. In the study, a modified DeGroot model is proposed by considering a real-time opinion injection from an intruder, and it is proved that the opinions of all the individuals converge to the opinion of the intruder. A set of ‘key’ individuals influenced by the intruder is then found such that the convergence speed on the malicious opinion is maximized. Further, a defense mechanism for each individual is proposed and the steady-state opinion gap of the individuals is obtained. Mumerical examples show the relation of the node role and the opinion convergence speed, and verify the effectiveness of the defense mechanism.
This paper concerns the opinion evolution dynamics with malicious opinion injection in social networks. In the study, a modified DeGroot model is proposed by considering a real-time opinion injection from an intruder, and it is proved that the opinions of all the individuals converge to the opinion of the intruder. A set of ‘key’ individuals influenced by the intruder is then found such that the convergence speed on the malicious opinion is maximized. Further, a defense mechanism for each individual is proposed and the steady-state opinion gap of the individuals is obtained. Mumerical examples show the relation of the node role and the opinion convergence speed, and verify the effectiveness of the defense mechanism.
2020, 49(6): 934-941.
doi: 10.12178/1001-0548.2019124
Abstract:
Thermoelectric material as a new clean energy material can realize transfer between electricity and heat. Skutterudite compound is one of the best thermoelectric materials in the middle temperature range due to its unique cage-like structure. In this paper, the thermoelectric properties of the CoSb3 based skutterudites compounds are reviewed, and the main ways to improve the thermoelectric properties of the CoSb3 based skutterudites compounds are summarized, including doped skutterudites, filled skutterudites and nanostructured skutterudites. Hope to extend to the application of thermoelectric skutterudites.
Thermoelectric material as a new clean energy material can realize transfer between electricity and heat. Skutterudite compound is one of the best thermoelectric materials in the middle temperature range due to its unique cage-like structure. In this paper, the thermoelectric properties of the CoSb3 based skutterudites compounds are reviewed, and the main ways to improve the thermoelectric properties of the CoSb3 based skutterudites compounds are summarized, including doped skutterudites, filled skutterudites and nanostructured skutterudites. Hope to extend to the application of thermoelectric skutterudites.
2020, 49(6): 942-948.
doi: 10.12178/1001-0548.2019201
Abstract:
This paper studies the detection of artificial prefabricated defect (rectangular defect) models and the fatigue defect (triangular defect) models. A three-dimensional numerical calculation model for artificial prefabricated rectangular defects and fatigue defects was established by COMSOL multiphysics. The depth and height of the crack defects are different in detail. The relationship with the magnetic flux density Z component is discussed, meanwhile, taking the defect-free model for comparison, the differential signal of the magnetic flux density Z component is analyzed. The finite element analysis model of the eddy current testing system established by COMSOL multiphysics is used to analyze the quantitative evolution of the flux density distribution of the two different cracks with the correspond crack height and depth. Both artificial prefabricated defect and fatigue defect on the surface are considered in this research, the achieved results is useful for deeply understanding the eddy current detection principle, and also can be applied in quantitative detection of natural defects in metal components.
This paper studies the detection of artificial prefabricated defect (rectangular defect) models and the fatigue defect (triangular defect) models. A three-dimensional numerical calculation model for artificial prefabricated rectangular defects and fatigue defects was established by COMSOL multiphysics. The depth and height of the crack defects are different in detail. The relationship with the magnetic flux density Z component is discussed, meanwhile, taking the defect-free model for comparison, the differential signal of the magnetic flux density Z component is analyzed. The finite element analysis model of the eddy current testing system established by COMSOL multiphysics is used to analyze the quantitative evolution of the flux density distribution of the two different cracks with the correspond crack height and depth. Both artificial prefabricated defect and fatigue defect on the surface are considered in this research, the achieved results is useful for deeply understanding the eddy current detection principle, and also can be applied in quantitative detection of natural defects in metal components.
2020, 49(6): 949-954.
doi: 10.12178/1001-0548.2020099
Abstract:
Nanosecond pulsed electric field (nsPEF) has been demonstrated to induce cancerous cell apoptosis, inhibit tumor growth and elicit antitumor immunity. Caused by the apoptosis mechanism of nanosecond pulse stimulation (NPS) therapy involving too many targets and the limitation of conventional detecting specific biomarker technology, the apoptosis mechanism of NPS has not been reached consensus and there is no efficient method for researching it. In order to gain further insight into the apoptosis mechanism, the paper has studied the murine melanoma B16 cells stimulated by nsPEF by using antibody array. A hypothesis for the pathway of NPS with three steps for the apoptosis mechanism was introduced. The research method possess the unique advantages in this kind of researches with huge number of targets, the obtained results could be helpful for designing future NPS therapies and aid in targeting the specific molecules with the optimal pulse parameters to obtain best therapeutic effect.
Nanosecond pulsed electric field (nsPEF) has been demonstrated to induce cancerous cell apoptosis, inhibit tumor growth and elicit antitumor immunity. Caused by the apoptosis mechanism of nanosecond pulse stimulation (NPS) therapy involving too many targets and the limitation of conventional detecting specific biomarker technology, the apoptosis mechanism of NPS has not been reached consensus and there is no efficient method for researching it. In order to gain further insight into the apoptosis mechanism, the paper has studied the murine melanoma B16 cells stimulated by nsPEF by using antibody array. A hypothesis for the pathway of NPS with three steps for the apoptosis mechanism was introduced. The research method possess the unique advantages in this kind of researches with huge number of targets, the obtained results could be helpful for designing future NPS therapies and aid in targeting the specific molecules with the optimal pulse parameters to obtain best therapeutic effect.
2020, 49(6): 955-960.
doi: 10.12178/1001-0548.2020013
Abstract:
Methamphetamine is a new drug that can cause brain disfunction and even death. Most of the current studies only conducted enterprise resource planing (ERP) analysis on the electroencephalogram (EEG) signals of methamphetamine addicts, and rarely classified the EEG signals of methamphetamine addicts. This study conducted an ERP analysis on the EEG signals of two types of subjects, and then extracted to the time domain, frequency domain and wavelet coefficients of the EEG signals within this range for each experiment. The classification accuracy can reach higher than 80%. In addition, 4 of the 5 electrodes with a classification accuracy of more than 75% were located in the frontal lobe, indicating that methamphetamine had the most severe impact on the frontal lobe compared to other brain regions. Therefore, the methamphetamine may mainly damage the brain's thinking and cognitive execution functions. The C3 electrode is projected mainly in the primary motor cortex region, methamphetamine may have an impact on the function of exercise execution of the methamphetamine addicts. The result provides some theory to further study on the effects of methamphetamine.
Methamphetamine is a new drug that can cause brain disfunction and even death. Most of the current studies only conducted enterprise resource planing (ERP) analysis on the electroencephalogram (EEG) signals of methamphetamine addicts, and rarely classified the EEG signals of methamphetamine addicts. This study conducted an ERP analysis on the EEG signals of two types of subjects, and then extracted to the time domain, frequency domain and wavelet coefficients of the EEG signals within this range for each experiment. The classification accuracy can reach higher than 80%. In addition, 4 of the 5 electrodes with a classification accuracy of more than 75% were located in the frontal lobe, indicating that methamphetamine had the most severe impact on the frontal lobe compared to other brain regions. Therefore, the methamphetamine may mainly damage the brain's thinking and cognitive execution functions. The C3 electrode is projected mainly in the primary motor cortex region, methamphetamine may have an impact on the function of exercise execution of the methamphetamine addicts. The result provides some theory to further study on the effects of methamphetamine.
