Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS m...Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.展开更多
The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-n...The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.展开更多
Large Language Models(LLMs),exemplified by ChatGPT,have significantly reshaped text generation,particularly in the realm of writing assistance.While ethical considerations underscore the importance of transparently ac...Large Language Models(LLMs),exemplified by ChatGPT,have significantly reshaped text generation,particularly in the realm of writing assistance.While ethical considerations underscore the importance of transparently acknowledging LLM use,especially in scientific communication,genuine acknowledgment remains infrequent.A potential avenue to encourage accurate acknowledging of LLM-assisted writing involves employing automated detectors.Our evaluation of four cutting-edge LLM-generated text detectors reveals their suboptimal performance compared to a simple ad-hoc detector designed to identify abrupt writing style changes around the time of LLM proliferation.We contend that the development of specialized detectors exclusively dedicated to LLM-assisted writing detection is necessary.Such detectors could play a crucial role in fostering more authentic recognition of LLM involvement in scientific communication,addressing the current challenges in acknowledgment practices.展开更多
This article proposes and demonstrates a retrodirective array(RDA)for two-way wireless communication with automatic beam tracking.The proposed RDA is enabled by specifically designed chips made using a domestic comple...This article proposes and demonstrates a retrodirective array(RDA)for two-way wireless communication with automatic beam tracking.The proposed RDA is enabled by specifically designed chips made using a domestic complementary metal-oxide semiconductor(CMOS)process.The highly integrated CMOS chip includes a receiving(Rx)chain,a transmitting(Tx)chain,and a unique tracking phaselocked loop(PLL)for the crucial conjugated phase recovery in the RDA.This article also proposes a method to reduce the beam pointing error(BPE)in a conventional RDA.To validate the above ideas simply yet without loss of generality,a 2.4 GHz RDA is demonstrated through two-way communication links between the Rx and Tx chains,and an on-chip quadrature coupler is designed to achieve a nonretrodirective signal suppression of 23 dBc.The experimental results demonstrate that the proposed RDA,which incorporates domestically manufactured low-cost 0.18 lm CMOS chips,is capable of automatically tracking beams covering±40with a reduced BPE.Each CMOS chip in the RDA has a compact size of 4.62 mm^(2) and a low power consumption of 0.15 W.To the best of the authors’knowledge,this is the first research to demonstrate an RDA with a fully customized CMOS chip for wireless communication with automatic beam tracking。展开更多
Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior perfo...Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.展开更多
Blades are one of the important components on aircraft engines.If they break due to vibration failure,the normal operation of the entire engine will be offected.Therefore,it is necessary to measure their natural frequ...Blades are one of the important components on aircraft engines.If they break due to vibration failure,the normal operation of the entire engine will be offected.Therefore,it is necessary to measure their natural frequency before installing them on the engine to avoid resonance.At present,most blade vibration testing systems require manual operation by operators,which has high requirements for operators and the testing process is also very cumbersome.Therefore,the testing efficiency is low and cannot meet the needs of efficient testing.To solve the current problems of low testing efficiency and high operational requirements,a high-precision and high-efficiency automatic test system is designed.The testing accuracy of this system can reach ±1%,and the testing efficiency is improved by 37% compared to manual testing.Firstly,the influence of compression force and vibration exciter position on natural frequency test is analyzed by amplitude-frequency curve,so as to calibrate servo cylinder and fourdimensional motion platform.Secondly,the sine wave signal is used as the excitation to sweep the blade linearly,and the natural frequency is determined by the amplitude peak in the frequency domain.Finally,the accuracy experiment and efficiency experiment are carried out on the developed test system,whose results verify its high efficiency and high precision.展开更多
Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system,and its incidence rate ranks ninth in the world.In recent years,the continuous development of hyperspectral imaging technology ...Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system,and its incidence rate ranks ninth in the world.In recent years,the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer.In this study,based on microscopic hyperspectral data,an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed.Support vector machine(SVM)is used to build classification models and compare the classification performance of spectral feature,spectral and shape fusion feature,and the fusion feature proposed in this paper on the same classifier.The results show that the sensitivity,specificity,and accuracy of our classification algorithm based on shape and color fusion features are 0.952,0.897,and 0.920,respectively,which are better than the classification algorithm only using spectral features.Therefore,this study can effectively extract the cell features of bladder urothelial carcinoma smear,thus achieving automatic,real-time,and noninvasive detection of bladder tumor cells,and then helping doctors improve the e±ciency of pathological diagnosis of bladder urothelial cancer,and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease.展开更多
A system for fully automatic selection of welding specifications in resistance welding equipment has been developed to address the problem of workers frequently choosing the wrong specifications during manual welding ...A system for fully automatic selection of welding specifications in resistance welding equipment has been developed to address the problem of workers frequently choosing the wrong specifications during manual welding of multiple parts on a single machine in automobile factories. The system incorporates an automatic recognition system for different workpiece materials using the added machine fixture,visual detection system for nuts and bolts,and secondary graphical confirmation to ensure the correctness of specification calling. This system achieves reliable,fully automatic selection of welding specifications in resistance welding equipment and has shown significant effects in improving welding quality for massproduced workpieces,while solving the problem of specification calling errors that can occur with traditional methods involving process charts and code adjustments. This system is particularly suitable for promoting applications in manual welding of multiple parts on a single machine in automobile factories,ensuring correct specification calling and welding quality.展开更多
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
Non-pillar mining technology with automatically formed roadway is a new mining method without coal pillar reservation and roadway excavation.The stability control of automatically formed roadway is the key to the succ...Non-pillar mining technology with automatically formed roadway is a new mining method without coal pillar reservation and roadway excavation.The stability control of automatically formed roadway is the key to the successful application of the new method.In order to realize the stability control of the roadway surrounding rock,the mechanical model of the roof and rib support structure is established,and the influence mechanism of the automatically formed roadway parameters on the compound force is revealed.On this basis,the roof and rib support structure technology of confined lightweight concrete is proposed,and its mechanical tests under different eccentricity are carried out.The results show that the bearing capacity of confined lightweight concrete specimens is basically the same as that of ordinary confined concrete specimens.The bearing capacity of confined lightweight concrete specimens under different eccentricities is 1.95 times higher than those of U-shaped steel specimens.By comparing the test results with the theoretical calculated results of the confined concrete,the calculation method of the bearing capacity for the confined lightweight concrete structure is selected.The design method of confined lightweight concrete support structure is established,and is successfully applied in the extra-large mine,Ningtiaota Coal Mine,China.展开更多
It is well known that femtosecond laser pulses can easily spontaneously induce deep-subwavelength periodic surface structures on transparent dielectrics but not on non-transparent semiconductors.Nevertheless,in this s...It is well known that femtosecond laser pulses can easily spontaneously induce deep-subwavelength periodic surface structures on transparent dielectrics but not on non-transparent semiconductors.Nevertheless,in this study,we demonstrate that using high-numerical-aperture 800 nm femtosecond laser direct writing with controlled pulse energy and scanning speed in the near-damage-threshold regime,polarization-dependent deep-subwavelength single grooves with linewidths of~180 nm can be controllably prepared on Si.Generally,the single-groove linewidth increases slightly with increase in the pulse energy and decrease in the scanning speed,whereas the single-groove depth significantly increases from~300 nm to~600 nm with decrease in the scanning speed,or even to over 1μm with multi-processing,indicating the characteristics of transverse clamping and longitudinal growth of such deep-subwavelength single grooves.Energy dispersive spectroscopy composition analysis of the near-groove region confirms that single-groove formation tends to be an ultrafast,non-thermal ablation process,and the oxidized deposits near the grooves are easy to clean up.Furthermore,the results,showing both the strong dependence of groove orientation on laser polarization and the occurrence of double-groove structures due to the interference of pre-formed orthogonal grooves,indicate that the extraordinary field enhancement of strong polarization sensitivity in the deep-subwavelength groove plays an important role in single-groove growth with high stability and collimation.展开更多
This study examines the writing abilities of Iranian intermediate Korean learners,specifically their performance in the compositional writing section of the Test of Proficiency in Korean(TOPIK).By utilizing the many F...This study examines the writing abilities of Iranian intermediate Korean learners,specifically their performance in the compositional writing section of the Test of Proficiency in Korean(TOPIK).By utilizing the many FACETS-Rasch Model,we meticulously analyzed nine writing samples from the 52nd TOPIK.These samples were evaluated using a modified rubric ranging from 0 to 3 based on predefined criteria for written composition.The results underscored that the sections on“appropriateness of spacing and spelling,”“relevance of vocabulary,”and“content diversity”presented the most significant challenges for the learners.On the other hand,“the quantity of writing”emerged as the least challenging aspect.These findings reveal substantial disparities in various aspects of writing proficiency among learners.The study not only pinpoints issue areas in writing skills,but also underscores the necessity of customized teaching strategies within the TOPIK framework to address these weaknesses.Consequently,it offers valuable insights that could bolster the effectiveness of teaching writing to Korean language learners.The findings of this study are not only significant for the field of language education,but also contribute to a deeper understanding of the challenges faced by intermediate learners and provide a roadmap for improving language instruction.展开更多
Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve it...Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.展开更多
In this paper,an improved sag control strategy based on automatic SOC equalization is proposed to solve the problems of slow SOC equalization and excessive bus voltage fluctuation amplitude and offset caused by load a...In this paper,an improved sag control strategy based on automatic SOC equalization is proposed to solve the problems of slow SOC equalization and excessive bus voltage fluctuation amplitude and offset caused by load and PV power variations in a stand-alone DC microgrid.The strategy includes primary and secondary control.Among them,the primary control suppresses the DC microgrid voltage fluctuation through the I and II section control,and the secondary control aims to correct the P-U curve of the energy storage system and the PV system,thus reducing the steady-state bus voltage excursion.The simulation results demonstrate that the proposed control strategy effectively achieves SOC balancing and enhances the immunity of bus voltage.The proposed strategy improves the voltage fluctuation suppression ability by approximately 39.4%and 43.1%under the PV power and load power fluctuation conditions,respectively.Furthermore,the steady-state deviation of the bus voltage,△U_(dc) is only 0.01–0.1 V,ensuring stable operation of the DC microgrid in fluctuating power environments.展开更多
[Objectives]To monitor the stability of open-pit coal mine slopes in real time and ensure the safety of coal mine production.[Methods]The automatic monitoring system of coal mine slope was explored in depth,and the co...[Objectives]To monitor the stability of open-pit coal mine slopes in real time and ensure the safety of coal mine production.[Methods]The automatic monitoring system of coal mine slope was explored in depth,and the core functions of the system were designed comprehensively.According to the design function of the automatic monitoring system,the slope automatic monitoring system was constructed.Besides,in accordance with the actual situation of the slope,the monitoring frequency of slopes was set scientifically,and the key indicators such as rainfall,deep displacement and surface displacement of the slopes were monitored in an all-round and multi-angle way.[Results]During the monitoring period,the overall condition of the slope remained good,and no landslides or other geological disasters occurred.At the same time,the overall rainfall in the slope area remained low.In terms of monitoring data,the horizontal displacement and settlement of the slopes increased first and then tended to be stable.Specifically,the maximum horizontal displacement during the monitoring period was 22.74 mm,while the maximum settlement was 18.65 mm.[Conclusions]The automatic slope monitoring system has obtained remarkable achievements in practical application.It not only improves the accuracy and efficiency of slope stability monitoring,but also provides valuable reference experience for similar projects.展开更多
Little is known about how the assessment modality,i.e.,computer-based(CB)and paper-based(PB)tests,affects language teachers’scorings,perceptions,and preferences and,therefore,the validity and fairness of classroom wr...Little is known about how the assessment modality,i.e.,computer-based(CB)and paper-based(PB)tests,affects language teachers’scorings,perceptions,and preferences and,therefore,the validity and fairness of classroom writing assessments.The present mixed-methods study used Shaw and Weir’s(2007)sociocognitive writing test validation framework to examine the scoring and consequential validity evidence of CB and PB writing tests in EFL classroom assessment in higher education.Original handwritten and word-processed texts of 38 EFL university students were transcribed to their opposite format and assessed by three language lecturers(N=456 texts,152 per teacher)to examine the scoring validity of CB and PB tests.The teachers’perceptions of text quality and preferences for assessment modality accounted for the consequential validity evidence of both tests.Findings revealed that the assessment modality impacted teachers’scorings,perceptions,and preferences.The teachers awarded higher scores to original and transcribed handwritten texts,particularly text organization and language use.The teachers’perceptions of text quality differed from their ratings,and physical,psychological,and experiential characteristics influenced their preferences for assessment modality.The results have implications for the validity and fairness of CB and PB writing tests and teachers’assessment practices.展开更多
Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thic...Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.展开更多
Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary mea...Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.展开更多
基金supported by the Platform Development Foundation of the China Institute for Radiation Protection(No.YP21030101)the National Natural Science Foundation of China(General Program)(Nos.12175114,U2167209)+1 种基金the National Key R&D Program of China(No.2021YFF0603600)the Tsinghua University Initiative Scientific Research Program(No.20211080081).
文摘Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation 2022M720419 to provide fund for conducting experiments。
文摘The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.
文摘Large Language Models(LLMs),exemplified by ChatGPT,have significantly reshaped text generation,particularly in the realm of writing assistance.While ethical considerations underscore the importance of transparently acknowledging LLM use,especially in scientific communication,genuine acknowledgment remains infrequent.A potential avenue to encourage accurate acknowledging of LLM-assisted writing involves employing automated detectors.Our evaluation of four cutting-edge LLM-generated text detectors reveals their suboptimal performance compared to a simple ad-hoc detector designed to identify abrupt writing style changes around the time of LLM proliferation.We contend that the development of specialized detectors exclusively dedicated to LLM-assisted writing detection is necessary.Such detectors could play a crucial role in fostering more authentic recognition of LLM involvement in scientific communication,addressing the current challenges in acknowledgment practices.
基金supported in part by the National Key Research and Development Program of China(2019YFB2204701)in part by the National Natural Science Foundation of China(61831006,62022023,and 62250610223)in part by the Big Data Computing Center at Southeast University for numerical calculation.
文摘This article proposes and demonstrates a retrodirective array(RDA)for two-way wireless communication with automatic beam tracking.The proposed RDA is enabled by specifically designed chips made using a domestic complementary metal-oxide semiconductor(CMOS)process.The highly integrated CMOS chip includes a receiving(Rx)chain,a transmitting(Tx)chain,and a unique tracking phaselocked loop(PLL)for the crucial conjugated phase recovery in the RDA.This article also proposes a method to reduce the beam pointing error(BPE)in a conventional RDA.To validate the above ideas simply yet without loss of generality,a 2.4 GHz RDA is demonstrated through two-way communication links between the Rx and Tx chains,and an on-chip quadrature coupler is designed to achieve a nonretrodirective signal suppression of 23 dBc.The experimental results demonstrate that the proposed RDA,which incorporates domestically manufactured low-cost 0.18 lm CMOS chips,is capable of automatically tracking beams covering±40with a reduced BPE.Each CMOS chip in the RDA has a compact size of 4.62 mm^(2) and a low power consumption of 0.15 W.To the best of the authors’knowledge,this is the first research to demonstrate an RDA with a fully customized CMOS chip for wireless communication with automatic beam tracking。
基金supported by the Beijing Natural Science Foundation (L202003)National Natural Science Foundation of China (No. 31700479)。
文摘Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.
基金supported by the National Natural Science Foundation of China (No.51975293)Aeronautical Science Foundation of China (No.2019ZD052010)Postgraduate Research & Practice Innovation Program of NUAA (No.xcxjh20230502)。
文摘Blades are one of the important components on aircraft engines.If they break due to vibration failure,the normal operation of the entire engine will be offected.Therefore,it is necessary to measure their natural frequency before installing them on the engine to avoid resonance.At present,most blade vibration testing systems require manual operation by operators,which has high requirements for operators and the testing process is also very cumbersome.Therefore,the testing efficiency is low and cannot meet the needs of efficient testing.To solve the current problems of low testing efficiency and high operational requirements,a high-precision and high-efficiency automatic test system is designed.The testing accuracy of this system can reach ±1%,and the testing efficiency is improved by 37% compared to manual testing.Firstly,the influence of compression force and vibration exciter position on natural frequency test is analyzed by amplitude-frequency curve,so as to calibrate servo cylinder and fourdimensional motion platform.Secondly,the sine wave signal is used as the excitation to sweep the blade linearly,and the natural frequency is determined by the amplitude peak in the frequency domain.Finally,the accuracy experiment and efficiency experiment are carried out on the developed test system,whose results verify its high efficiency and high precision.
基金Bethune Medical Engineering and Instrument Center Fund(E10133Y8H0)Jilin province science and technology development plan project(20210204216YY,20210204146YY).
文摘Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system,and its incidence rate ranks ninth in the world.In recent years,the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer.In this study,based on microscopic hyperspectral data,an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed.Support vector machine(SVM)is used to build classification models and compare the classification performance of spectral feature,spectral and shape fusion feature,and the fusion feature proposed in this paper on the same classifier.The results show that the sensitivity,specificity,and accuracy of our classification algorithm based on shape and color fusion features are 0.952,0.897,and 0.920,respectively,which are better than the classification algorithm only using spectral features.Therefore,this study can effectively extract the cell features of bladder urothelial carcinoma smear,thus achieving automatic,real-time,and noninvasive detection of bladder tumor cells,and then helping doctors improve the e±ciency of pathological diagnosis of bladder urothelial cancer,and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease.
文摘A system for fully automatic selection of welding specifications in resistance welding equipment has been developed to address the problem of workers frequently choosing the wrong specifications during manual welding of multiple parts on a single machine in automobile factories. The system incorporates an automatic recognition system for different workpiece materials using the added machine fixture,visual detection system for nuts and bolts,and secondary graphical confirmation to ensure the correctness of specification calling. This system achieves reliable,fully automatic selection of welding specifications in resistance welding equipment and has shown significant effects in improving welding quality for massproduced workpieces,while solving the problem of specification calling errors that can occur with traditional methods involving process charts and code adjustments. This system is particularly suitable for promoting applications in manual welding of multiple parts on a single machine in automobile factories,ensuring correct specification calling and welding quality.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
基金Project(2023YFC2907600)supported by the National Key Research and Development Program of ChinaProjects(42077267,42277174,52074164)supported by the National Natural Science Foundation of ChinaProject(2024JCCXSB01)supported by the Fundamental Research Funds for the Central Universities,China。
文摘Non-pillar mining technology with automatically formed roadway is a new mining method without coal pillar reservation and roadway excavation.The stability control of automatically formed roadway is the key to the successful application of the new method.In order to realize the stability control of the roadway surrounding rock,the mechanical model of the roof and rib support structure is established,and the influence mechanism of the automatically formed roadway parameters on the compound force is revealed.On this basis,the roof and rib support structure technology of confined lightweight concrete is proposed,and its mechanical tests under different eccentricity are carried out.The results show that the bearing capacity of confined lightweight concrete specimens is basically the same as that of ordinary confined concrete specimens.The bearing capacity of confined lightweight concrete specimens under different eccentricities is 1.95 times higher than those of U-shaped steel specimens.By comparing the test results with the theoretical calculated results of the confined concrete,the calculation method of the bearing capacity for the confined lightweight concrete structure is selected.The design method of confined lightweight concrete support structure is established,and is successfully applied in the extra-large mine,Ningtiaota Coal Mine,China.
基金Project supported by the Natural Science Foundation of Guangdong Province (Grant No.2021A1515012335)the National Natural Science Foundation of China (Grant No.11274400)+2 种基金Pearl River S&T Nova Program of Guangzhou (Grant No.201506010059)State Key Laboratory of High Field Laser Physics (Shanghai Institute of Optics and Fine Mechanics)State Key Laboratory of Optoelectronic Materials and Technologies (Sun Yat-Sen University)。
文摘It is well known that femtosecond laser pulses can easily spontaneously induce deep-subwavelength periodic surface structures on transparent dielectrics but not on non-transparent semiconductors.Nevertheless,in this study,we demonstrate that using high-numerical-aperture 800 nm femtosecond laser direct writing with controlled pulse energy and scanning speed in the near-damage-threshold regime,polarization-dependent deep-subwavelength single grooves with linewidths of~180 nm can be controllably prepared on Si.Generally,the single-groove linewidth increases slightly with increase in the pulse energy and decrease in the scanning speed,whereas the single-groove depth significantly increases from~300 nm to~600 nm with decrease in the scanning speed,or even to over 1μm with multi-processing,indicating the characteristics of transverse clamping and longitudinal growth of such deep-subwavelength single grooves.Energy dispersive spectroscopy composition analysis of the near-groove region confirms that single-groove formation tends to be an ultrafast,non-thermal ablation process,and the oxidized deposits near the grooves are easy to clean up.Furthermore,the results,showing both the strong dependence of groove orientation on laser polarization and the occurrence of double-groove structures due to the interference of pre-formed orthogonal grooves,indicate that the extraordinary field enhancement of strong polarization sensitivity in the deep-subwavelength groove plays an important role in single-groove growth with high stability and collimation.
文摘This study examines the writing abilities of Iranian intermediate Korean learners,specifically their performance in the compositional writing section of the Test of Proficiency in Korean(TOPIK).By utilizing the many FACETS-Rasch Model,we meticulously analyzed nine writing samples from the 52nd TOPIK.These samples were evaluated using a modified rubric ranging from 0 to 3 based on predefined criteria for written composition.The results underscored that the sections on“appropriateness of spacing and spelling,”“relevance of vocabulary,”and“content diversity”presented the most significant challenges for the learners.On the other hand,“the quantity of writing”emerged as the least challenging aspect.These findings reveal substantial disparities in various aspects of writing proficiency among learners.The study not only pinpoints issue areas in writing skills,but also underscores the necessity of customized teaching strategies within the TOPIK framework to address these weaknesses.Consequently,it offers valuable insights that could bolster the effectiveness of teaching writing to Korean language learners.The findings of this study are not only significant for the field of language education,but also contribute to a deeper understanding of the challenges faced by intermediate learners and provide a roadmap for improving language instruction.
基金supported in part by National Key Research and Development Program of China under Grant 2021YFB2900404.
文摘Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.
基金supported by the NationalNatural Science Foundation of China(No.52067013)the Natural Science Foundation of Gansu Province(No.20JR5RA395)as well as the Tianyou Innovation Team of Lanzhou Jiaotong University(TY202010).
文摘In this paper,an improved sag control strategy based on automatic SOC equalization is proposed to solve the problems of slow SOC equalization and excessive bus voltage fluctuation amplitude and offset caused by load and PV power variations in a stand-alone DC microgrid.The strategy includes primary and secondary control.Among them,the primary control suppresses the DC microgrid voltage fluctuation through the I and II section control,and the secondary control aims to correct the P-U curve of the energy storage system and the PV system,thus reducing the steady-state bus voltage excursion.The simulation results demonstrate that the proposed control strategy effectively achieves SOC balancing and enhances the immunity of bus voltage.The proposed strategy improves the voltage fluctuation suppression ability by approximately 39.4%and 43.1%under the PV power and load power fluctuation conditions,respectively.Furthermore,the steady-state deviation of the bus voltage,△U_(dc) is only 0.01–0.1 V,ensuring stable operation of the DC microgrid in fluctuating power environments.
文摘[Objectives]To monitor the stability of open-pit coal mine slopes in real time and ensure the safety of coal mine production.[Methods]The automatic monitoring system of coal mine slope was explored in depth,and the core functions of the system were designed comprehensively.According to the design function of the automatic monitoring system,the slope automatic monitoring system was constructed.Besides,in accordance with the actual situation of the slope,the monitoring frequency of slopes was set scientifically,and the key indicators such as rainfall,deep displacement and surface displacement of the slopes were monitored in an all-round and multi-angle way.[Results]During the monitoring period,the overall condition of the slope remained good,and no landslides or other geological disasters occurred.At the same time,the overall rainfall in the slope area remained low.In terms of monitoring data,the horizontal displacement and settlement of the slopes increased first and then tended to be stable.Specifically,the maximum horizontal displacement during the monitoring period was 22.74 mm,while the maximum settlement was 18.65 mm.[Conclusions]The automatic slope monitoring system has obtained remarkable achievements in practical application.It not only improves the accuracy and efficiency of slope stability monitoring,but also provides valuable reference experience for similar projects.
文摘Little is known about how the assessment modality,i.e.,computer-based(CB)and paper-based(PB)tests,affects language teachers’scorings,perceptions,and preferences and,therefore,the validity and fairness of classroom writing assessments.The present mixed-methods study used Shaw and Weir’s(2007)sociocognitive writing test validation framework to examine the scoring and consequential validity evidence of CB and PB writing tests in EFL classroom assessment in higher education.Original handwritten and word-processed texts of 38 EFL university students were transcribed to their opposite format and assessed by three language lecturers(N=456 texts,152 per teacher)to examine the scoring validity of CB and PB tests.The teachers’perceptions of text quality and preferences for assessment modality accounted for the consequential validity evidence of both tests.Findings revealed that the assessment modality impacted teachers’scorings,perceptions,and preferences.The teachers awarded higher scores to original and transcribed handwritten texts,particularly text organization and language use.The teachers’perceptions of text quality differed from their ratings,and physical,psychological,and experiential characteristics influenced their preferences for assessment modality.The results have implications for the validity and fairness of CB and PB writing tests and teachers’assessment practices.
基金Supported by the National Natural Science Foundation of China(42272110)CNPC-China University of Petroleum(Beijing)Strategic Cooperation Project(ZLZX2020-02).
文摘Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.
基金Supported by Shandong Province Key R and D Program,No.2021SFGC0504Shandong Provincial Natural Science Foundation,No.ZR2021MF079Science and Technology Development Plan of Jinan(Clinical Medicine Science and Technology Innovation Plan),No.202225054.
文摘Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.