A real-time data compression wireless sensor network based on Lempel-Ziv-Welch encoding(LZW)algorithm is designed for the increasing data volume of terminal nodes when using ZigBee for long-distance wireless communica...A real-time data compression wireless sensor network based on Lempel-Ziv-Welch encoding(LZW)algorithm is designed for the increasing data volume of terminal nodes when using ZigBee for long-distance wireless communication.The system consists of a terminal node,a router,a coordinator,and an upper computer.The terminal node is responsible for storing and sending the collected data after the LZW compression algorithm is compressed;The router is responsible for the relay of data in the wireless network;The coordinator is responsible for sending the received data to the upper computer.In terms of network function realization,the development and configuration of CC2530 chips on terminal nodes,router nodes,and coordinator nodes are completed using the Z-stack protocol stack,and the network is successfully organized.Through the final simulation analysis and test verification,the system realizes the wireless acquisition and storage of remote data,and reduces the network occupancy rate through the data compression,which has a certain practical value and application prospects.展开更多
Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive da...Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.展开更多
A new real-time algorithm of data compression, including the segment-normalized logical compression and socalled 'one taken from two samples',is presented for broadband high dynamic seismic recordings. This al...A new real-time algorithm of data compression, including the segment-normalized logical compression and socalled 'one taken from two samples',is presented for broadband high dynamic seismic recordings. This algorithm was tested by numerical simulation and data observed. Its results demonstrate that total errors in recovery data are less than 1% of original data in time domain,0.5% in frequency domain, when using these two methods together.Its compression ratio is greater than 3.The data compression softwares based on the algorithm have been used in the GDS-1000 portable broadband digital seismograph.展开更多
The uniaxial compressive strength(UCS)of rocks is a vital geomechanical parameter widely used for rock mass classification,stability analysis,and engineering design in rock engineering.Various UCS testing methods and ...The uniaxial compressive strength(UCS)of rocks is a vital geomechanical parameter widely used for rock mass classification,stability analysis,and engineering design in rock engineering.Various UCS testing methods and apparatuses have been proposed over the past few decades.The objective of the present study is to summarize the status and development in theories,test apparatuses,data processing of the existing testing methods for UCS measurement.It starts with elaborating the theories of these test methods.Then the test apparatus and development trends for UCS measurement are summarized,followed by a discussion on rock specimens for test apparatus,and data processing methods.Next,the method selection for UCS measurement is recommended.It reveals that the rock failure mechanism in the UCS testing methods can be divided into compression-shear,compression-tension,composite failure mode,and no obvious failure mode.The trends of these apparatuses are towards automation,digitization,precision,and multi-modal test.Two size correction methods are commonly used.One is to develop empirical correlation between the measured indices and the specimen size.The other is to use a standard specimen to calculate the size correction factor.Three to five input parameters are commonly utilized in soft computation models to predict the UCS of rocks.The selection of the test methods for the UCS measurement can be carried out according to the testing scenario and the specimen size.The engineers can gain a comprehensive understanding of the UCS testing methods and its potential developments in various rock engineering endeavors.展开更多
Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their perform...Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.展开更多
In this study,the micro-failure process and failure mechanism of a typical brittle rock under uniaxial compression are investigated via continuous real-time measurement of wave velocities.The experimental results indi...In this study,the micro-failure process and failure mechanism of a typical brittle rock under uniaxial compression are investigated via continuous real-time measurement of wave velocities.The experimental results indicate that the evolutions of wave velocities became progressively anisotropic under uniaxial loading due to the direction-dependent development of micro-damage.A wave velocity model considering the inner anisotropic crack evolution is proposed to accurately describe the variations of wave velocities during uniaxial compression testing.Based on which,the effective elastic parameters are inferred by a transverse isotropic constitutive model,and the evolutions of the crack density are inversed using a self-consistent damage model.It is found that the propagation of axial cracks dominates the failure process of brittle rock under uniaxial loading and oblique shear cracks develop with the appearance of macrocrack.展开更多
Test data compression and test resource partitioning （TRP） are essential to reduce the amount of test data in system-on-chip testing. A novel variable-to-variable-length compression codes is designed as advanced fre...Test data compression and test resource partitioning （TRP） are essential to reduce the amount of test data in system-on-chip testing. A novel variable-to-variable-length compression codes is designed as advanced fre- quency-directed run-length （AFDR） codes. Different [rom frequency-directed run-length （FDR） codes, AFDR encodes both 0- and 1-runs and uses the same codes to the equal length runs. It also modifies the codes for 00 and 11 to improve the compression performance. Experimental results for ISCAS 89 benchmark circuits show that AFDR codes achieve higher compression ratio than FDR and other compression codes.展开更多
The interleaving/multiplexing technique was used to realize a 200?MHz real time data acquisition system. Two 100?MHz ADC modules worked parallelly and every ADC plays out data in ping pang fashion. The design improv...The interleaving/multiplexing technique was used to realize a 200?MHz real time data acquisition system. Two 100?MHz ADC modules worked parallelly and every ADC plays out data in ping pang fashion. The design improved the system conversion rata to 200?MHz and reduced the speed of data transporting and storing to 50?MHz. The high speed HDPLD and ECL logic parts were used to control system timing and the memory address. The multi layer print board and the shield were used to decrease interference produced by the high speed circuit. The system timing was designed carefully. The interleaving/multiplexing technique could improve the system conversion rata greatly while reducing the speed of external digital interfaces greatly. The design resolved the difficulties in high speed system effectively. The experiment proved the data acquisition system is stable and accurate.展开更多
This paper presents a new test data compression/decompression method for SoC testing,called hybrid run length codes. The method makes a full analysis of the factors which influence test parameters：compression ratio,t...This paper presents a new test data compression/decompression method for SoC testing,called hybrid run length codes. The method makes a full analysis of the factors which influence test parameters：compression ratio,test application time, and area overhead. To improve the compression ratio, the new method is based on variable-to-variable run length codes,and a novel algorithm is proposed to reorder the test vectors and fill the unspecified bits in the pre-processing step. With a novel on-chip decoder, low test application time and low area overhead are obtained by hybrid run length codes. Finally, an experimental comparison on ISCAS 89 benchmark circuits validates the proposed method展开更多
In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ...In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.展开更多
Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great s...Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great significance for exploiting and protecting the ocean.We used hourly mean wave height,temperature,and pressure real-time observation data taken in the Xiaomaidao station(in Qingdao,China)from June 1,2017,to May 31,2018,to explore the data quality using eight quality control methods,and to discriminate the most effective method for Xiaomaidao station.After using the eight quality control methods,the percentages of the mean wave height,temperature,and pressure data that passed the tests were 89.6%,88.3%,and 98.6%,respectively.With the marine disaster(wave alarm report)data,the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions.The mean wave height is often affected by dynamic marine disasters,so the continuity test method is not effective.The correlation test with other related parameters would be more useful for the mean wave height.展开更多
A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, i...A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation （EBCOT） . Simulation results show that EDD is more suitable for non-stationary image data compression.展开更多
NC code or STL file can be generated directly from measuring data in a fastreverse-engineering mode. Compressing the massive data from laser scanner is the key of the newmode. An adaptive compression method based on t...NC code or STL file can be generated directly from measuring data in a fastreverse-engineering mode. Compressing the massive data from laser scanner is the key of the newmode. An adaptive compression method based on triangulated-surfaces model is put forward.Normal-vector angles between triangles are computed to find prime vertices for removal. Ring datastructure is adopted to save massive data effectively. It allows the efficient retrieval of allneighboring vertices and triangles of a given vertices. To avoid long and thin triangles, a newre-triangulation approach based on normalized minimum-vertex-distance is proposed, in which thevertex distance and interior angle of triangle are considered. Results indicate that the compressionmethod has high efficiency and can get reliable precision. The method can be applied in fastreverse engineering to acquire an optimal subset of the original massive data.展开更多
The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information ...The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.展开更多
Vector quantization （VQ） is an important data compression method. The key of the encoding of VQ is to find the closest vector among N vectors for a feature vector. Many classical linear search algorithms take O（N）...Vector quantization （VQ） is an important data compression method. The key of the encoding of VQ is to find the closest vector among N vectors for a feature vector. Many classical linear search algorithms take O（N） steps of distance computing between two vectors. The quantum VQ iteration and corresponding quantum VQ encoding algorithm that takes O（√N） steps are presented in this paper. The unitary operation of distance computing can be performed on a number of vectors simultaneously because the quantum state exists in a superposition of states. The quantum VQ iteration comprises three oracles, by contrast many quantum algorithms have only one oracle, such as Shor＇s factorization algorithm and Grover＇s algorithm. Entanglement state is generated and used, by contrast the state in Grover＇s algorithm is not an entanglement state. The quantum VQ iteration is a rotation over subspace, by contrast the Grover iteration is a rotation over global space. The quantum VQ iteration extends the Grover iteration to the more complex search that requires more oracles. The method of the quantum VQ iteration is universal.展开更多
The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the...The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment.In engineering scenarios,co-frequency vibration faults are highlighted by rotational frequency and are difficult to identify,and existing intelligent methods require more hardware conditions and are exclusively time-consuming.Therefore,Lightweight-convolutional neural networks(LW-CNN)algorithm is proposed in this paper to achieve real-time fault diagnosis.The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method.Based on LW-CNN and data augmentation,the real-time intelligent diagnosis of co-frequency is realized.Moreover,a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis.It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.展开更多
Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of i...Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.展开更多
Process data compression and trending are essential for improving control system performances. Swing Door Trending (SDT) algorithm is well designed to adapt the process trend while retaining the merit of simplicity. B...Process data compression and trending are essential for improving control system performances. Swing Door Trending (SDT) algorithm is well designed to adapt the process trend while retaining the merit of simplicity. But it cannot handle outliers and adapt to the fluctuations of actual data. An Improved SDT (ISDT) algorithm is proposed in this paper. The effectiveness and applicability of the ISDT algorithm are demonstrated by computations on both synthetic and real process data. By applying an adaptive recording limit as well as outliers-detecting rules, a higher compression ratio is achieved and outliers are identified and eliminated. The fidelity of the algorithm is also improved. It can be used both in online and batch mode, and integrated into existing software packages without change.展开更多
Considering the increasing use of information technology with established standards, such as TCP/IP and XML in modem industrial automation, we present a high cost performance solution with FPGA （field programmable ga...Considering the increasing use of information technology with established standards, such as TCP/IP and XML in modem industrial automation, we present a high cost performance solution with FPGA （field programmable gate array） implementation of a novel reliable real-time data transfer system based on EPA （Ethemet for plant automation） protocol and IEEE 1588 standard. This combination can provide more predictable and real-time communication between automation equipments and precise synchronization between devices. The designed EPA system has been verified on Xilinx Spartan3 XC3S1500 and it consumed 75% of the total slices. The experimental results show that the novel industrial control system achieves high synchronization precision and provides a 1.59-ps standard deviation between the master device and the slave ones. Such a real-time data transfer system is an excellent candidate for automation equipments which require precise synchronization based on Ethemet at a comparatively low price.展开更多
The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these v...The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these variables for accurate solar radiation prediction is very important. This paper presents a hybrid method for the compression of solar radiation using predictive analysis. The prediction of minute wise solar radiation is performed by using different models of Artificial Neural Networks (ANN), namely Multi-layer perceptron neural network (MLPNN), Cascade feed forward back propagation (CFNN) and Elman back propagation (ELMNN). Root mean square error (RMSE) is used to evaluate the prediction accuracy of the three ANN models used. The information and knowledge gained from the present study could improve the accuracy of analysis concerning climate studies and help in congestion control.展开更多
文摘A real-time data compression wireless sensor network based on Lempel-Ziv-Welch encoding(LZW)algorithm is designed for the increasing data volume of terminal nodes when using ZigBee for long-distance wireless communication.The system consists of a terminal node,a router,a coordinator,and an upper computer.The terminal node is responsible for storing and sending the collected data after the LZW compression algorithm is compressed;The router is responsible for the relay of data in the wireless network;The coordinator is responsible for sending the received data to the upper computer.In terms of network function realization,the development and configuration of CC2530 chips on terminal nodes,router nodes,and coordinator nodes are completed using the Z-stack protocol stack,and the network is successfully organized.Through the final simulation analysis and test verification,the system realizes the wireless acquisition and storage of remote data,and reduces the network occupancy rate through the data compression,which has a certain practical value and application prospects.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.
文摘A new real-time algorithm of data compression, including the segment-normalized logical compression and socalled 'one taken from two samples',is presented for broadband high dynamic seismic recordings. This algorithm was tested by numerical simulation and data observed. Its results demonstrate that total errors in recovery data are less than 1% of original data in time domain,0.5% in frequency domain, when using these two methods together.Its compression ratio is greater than 3.The data compression softwares based on the algorithm have been used in the GDS-1000 portable broadband digital seismograph.
基金the National Natural Science Foundation of China(Grant Nos.52308403 and 52079068)the Yunlong Lake Laboratory of Deep Underground Science and Engineering(No.104023005)the China Postdoctoral Science Foundation(Grant No.2023M731998)for funding provided to this work.
文摘The uniaxial compressive strength(UCS)of rocks is a vital geomechanical parameter widely used for rock mass classification,stability analysis,and engineering design in rock engineering.Various UCS testing methods and apparatuses have been proposed over the past few decades.The objective of the present study is to summarize the status and development in theories,test apparatuses,data processing of the existing testing methods for UCS measurement.It starts with elaborating the theories of these test methods.Then the test apparatus and development trends for UCS measurement are summarized,followed by a discussion on rock specimens for test apparatus,and data processing methods.Next,the method selection for UCS measurement is recommended.It reveals that the rock failure mechanism in the UCS testing methods can be divided into compression-shear,compression-tension,composite failure mode,and no obvious failure mode.The trends of these apparatuses are towards automation,digitization,precision,and multi-modal test.Two size correction methods are commonly used.One is to develop empirical correlation between the measured indices and the specimen size.The other is to use a standard specimen to calculate the size correction factor.Three to five input parameters are commonly utilized in soft computation models to predict the UCS of rocks.The selection of the test methods for the UCS measurement can be carried out according to the testing scenario and the specimen size.The engineers can gain a comprehensive understanding of the UCS testing methods and its potential developments in various rock engineering endeavors.
文摘Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.
基金Projects(41502283,41772309)supported by the National Natural Science Foundation of ChinaProject(2017YFC1501302)supported by the National Key Research and Development Program of ChinaProject(2017ACA102)supported by the Major Program of Technological Innovation of Hubei Province,China。
文摘In this study,the micro-failure process and failure mechanism of a typical brittle rock under uniaxial compression are investigated via continuous real-time measurement of wave velocities.The experimental results indicate that the evolutions of wave velocities became progressively anisotropic under uniaxial loading due to the direction-dependent development of micro-damage.A wave velocity model considering the inner anisotropic crack evolution is proposed to accurately describe the variations of wave velocities during uniaxial compression testing.Based on which,the effective elastic parameters are inferred by a transverse isotropic constitutive model,and the evolutions of the crack density are inversed using a self-consistent damage model.It is found that the propagation of axial cracks dominates the failure process of brittle rock under uniaxial loading and oblique shear cracks develop with the appearance of macrocrack.
基金Supported by the National Natural Science Foundation of China(61076019,61106018)the Aeronautical Science Foundation of China(20115552031)+3 种基金the China Postdoctoral Science Foundation(20100481134)the Jiangsu Province Key Technology R&D Program(BE2010003)the Nanjing University of Aeronautics and Astronautics Research Funding(NS2010115)the Nanjing University of Aeronatics and Astronautics Initial Funding for Talented Faculty(1004-YAH10027)~~
文摘Test data compression and test resource partitioning （TRP） are essential to reduce the amount of test data in system-on-chip testing. A novel variable-to-variable-length compression codes is designed as advanced fre- quency-directed run-length （AFDR） codes. Different [rom frequency-directed run-length （FDR） codes, AFDR encodes both 0- and 1-runs and uses the same codes to the equal length runs. It also modifies the codes for 00 and 11 to improve the compression performance. Experimental results for ISCAS 89 benchmark circuits show that AFDR codes achieve higher compression ratio than FDR and other compression codes.
文摘The interleaving/multiplexing technique was used to realize a 200?MHz real time data acquisition system. Two 100?MHz ADC modules worked parallelly and every ADC plays out data in ping pang fashion. The design improved the system conversion rata to 200?MHz and reduced the speed of data transporting and storing to 50?MHz. The high speed HDPLD and ECL logic parts were used to control system timing and the memory address. The multi layer print board and the shield were used to decrease interference produced by the high speed circuit. The system timing was designed carefully. The interleaving/multiplexing technique could improve the system conversion rata greatly while reducing the speed of external digital interfaces greatly. The design resolved the difficulties in high speed system effectively. The experiment proved the data acquisition system is stable and accurate.
文摘This paper presents a new test data compression/decompression method for SoC testing,called hybrid run length codes. The method makes a full analysis of the factors which influence test parameters：compression ratio,test application time, and area overhead. To improve the compression ratio, the new method is based on variable-to-variable run length codes,and a novel algorithm is proposed to reorder the test vectors and fill the unspecified bits in the pre-processing step. With a novel on-chip decoder, low test application time and low area overhead are obtained by hybrid run length codes. Finally, an experimental comparison on ISCAS 89 benchmark circuits validates the proposed method
基金supported by CNPC-CZU Innovation Alliancesupported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology (2022YFC2806403)。
文摘In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.
基金Supported by the National Key Research and Development Program of China(Nos.2016YFC1402000,2018YFC1407003,2017YFC1405300)
文摘Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great significance for exploiting and protecting the ocean.We used hourly mean wave height,temperature,and pressure real-time observation data taken in the Xiaomaidao station(in Qingdao,China)from June 1,2017,to May 31,2018,to explore the data quality using eight quality control methods,and to discriminate the most effective method for Xiaomaidao station.After using the eight quality control methods,the percentages of the mean wave height,temperature,and pressure data that passed the tests were 89.6%,88.3%,and 98.6%,respectively.With the marine disaster(wave alarm report)data,the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions.The mean wave height is often affected by dynamic marine disasters,so the continuity test method is not effective.The correlation test with other related parameters would be more useful for the mean wave height.
基金This project was supported by the National Natural Science Foundation of China (60532060)Hainan Education Bureau Research Project (Hjkj200602)Hainan Natural Science Foundation (80551).
文摘A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation （EBCOT） . Simulation results show that EDD is more suitable for non-stationary image data compression.
基金This project is supported by Provincial Key Project of Science and Technology of Zhejiang(No.2003C21031).
文摘NC code or STL file can be generated directly from measuring data in a fastreverse-engineering mode. Compressing the massive data from laser scanner is the key of the newmode. An adaptive compression method based on triangulated-surfaces model is put forward.Normal-vector angles between triangles are computed to find prime vertices for removal. Ring datastructure is adopted to save massive data effectively. It allows the efficient retrieval of allneighboring vertices and triangles of a given vertices. To avoid long and thin triangles, a newre-triangulation approach based on normalized minimum-vertex-distance is proposed, in which thevertex distance and interior angle of triangle are considered. Results indicate that the compressionmethod has high efficiency and can get reliable precision. The method can be applied in fastreverse engineering to acquire an optimal subset of the original massive data.
文摘The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.
文摘Vector quantization （VQ） is an important data compression method. The key of the encoding of VQ is to find the closest vector among N vectors for a feature vector. Many classical linear search algorithms take O（N） steps of distance computing between two vectors. The quantum VQ iteration and corresponding quantum VQ encoding algorithm that takes O（√N） steps are presented in this paper. The unitary operation of distance computing can be performed on a number of vectors simultaneously because the quantum state exists in a superposition of states. The quantum VQ iteration comprises three oracles, by contrast many quantum algorithms have only one oracle, such as Shor＇s factorization algorithm and Grover＇s algorithm. Entanglement state is generated and used, by contrast the state in Grover＇s algorithm is not an entanglement state. The quantum VQ iteration is a rotation over subspace, by contrast the Grover iteration is a rotation over global space. The quantum VQ iteration extends the Grover iteration to the more complex search that requires more oracles. The method of the quantum VQ iteration is universal.
基金Supported by National Natural Science Foundation of China(Grant Nos.51875031,52242507)Beijing Municipal Natural Science Foundation of China(Grant No.3212010)Beijing Municipal Youth Backbone Personal Project of China(Grant No.2017000020124 G018).
文摘The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment.In engineering scenarios,co-frequency vibration faults are highlighted by rotational frequency and are difficult to identify,and existing intelligent methods require more hardware conditions and are exclusively time-consuming.Therefore,Lightweight-convolutional neural networks(LW-CNN)algorithm is proposed in this paper to achieve real-time fault diagnosis.The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method.Based on LW-CNN and data augmentation,the real-time intelligent diagnosis of co-frequency is realized.Moreover,a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis.It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.
基金This work is supported by the National Natural Science Foundation of China(Grant No.51991392)Key Deployment Projects of Chinese Academy of Sciences(Grant No.ZDRW-ZS-2021-3-3)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0904).
文摘Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.
基金The authors would like to acknowledge the support from Project“973”of the State Key Fundamental Research under grant G1998030415.
文摘Process data compression and trending are essential for improving control system performances. Swing Door Trending (SDT) algorithm is well designed to adapt the process trend while retaining the merit of simplicity. But it cannot handle outliers and adapt to the fluctuations of actual data. An Improved SDT (ISDT) algorithm is proposed in this paper. The effectiveness and applicability of the ISDT algorithm are demonstrated by computations on both synthetic and real process data. By applying an adaptive recording limit as well as outliers-detecting rules, a higher compression ratio is achieved and outliers are identified and eliminated. The fidelity of the algorithm is also improved. It can be used both in online and batch mode, and integrated into existing software packages without change.
文摘Considering the increasing use of information technology with established standards, such as TCP/IP and XML in modem industrial automation, we present a high cost performance solution with FPGA （field programmable gate array） implementation of a novel reliable real-time data transfer system based on EPA （Ethemet for plant automation） protocol and IEEE 1588 standard. This combination can provide more predictable and real-time communication between automation equipments and precise synchronization between devices. The designed EPA system has been verified on Xilinx Spartan3 XC3S1500 and it consumed 75% of the total slices. The experimental results show that the novel industrial control system achieves high synchronization precision and provides a 1.59-ps standard deviation between the master device and the slave ones. Such a real-time data transfer system is an excellent candidate for automation equipments which require precise synchronization based on Ethemet at a comparatively low price.
文摘The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these variables for accurate solar radiation prediction is very important. This paper presents a hybrid method for the compression of solar radiation using predictive analysis. The prediction of minute wise solar radiation is performed by using different models of Artificial Neural Networks (ANN), namely Multi-layer perceptron neural network (MLPNN), Cascade feed forward back propagation (CFNN) and Elman back propagation (ELMNN). Root mean square error (RMSE) is used to evaluate the prediction accuracy of the three ANN models used. The information and knowledge gained from the present study could improve the accuracy of analysis concerning climate studies and help in congestion control.
