The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.H...The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.展开更多
In erasure-coded storage systems,updating data requires parity maintenance,which often leads to significant I/O amplification due to“write-after-read”operations.Furthermore,scattered parity placement increases disk ...In erasure-coded storage systems,updating data requires parity maintenance,which often leads to significant I/O amplification due to“write-after-read”operations.Furthermore,scattered parity placement increases disk seek overhead during repair,resulting in degraded system performance.To address these challenges,this paper proposes a Cognitive Update and Repair Method(CURM)that leverages machine learning to classify files into writeonly,read-only,and read-write categories,enabling tailored update and repair strategies.For write-only and read-write files,CURM employs a data-differencemechanism combined with fine-grained I/O scheduling to minimize redundant read operations and mitigate I/O amplification.For read-write files,CURM further reserves adjacent disk space near parity blocks,supporting parallel reads and reducing disk seek overhead during repair.We implement CURM in a prototype system,Cognitive Update and Repair File System(CURFS),and conduct extensive experiments using realworld Network File System(NFS)and Microsoft Research(MSR)workloads on a 25-node cluster.Experimental results demonstrate that CURMimproves data update throughput by up to 82.52%,reduces recovery time by up to 47.47%,and decreases long-term storage overhead by more than 15% compared to state-of-the-art methods including Full Logging(FL),ParityLogging(PL),ParityLoggingwithReservedspace(PLR),andPARIX.These results validate the effectiveness of CURM in enhancing both update and repair performance,providing a scalable and efficient solution for large-scale erasure-coded storage systems.展开更多
With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud...With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.展开更多
Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a ...Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction.Conventional design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design level.However,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such systems.This research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment analysis.The proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user context.The design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user context.Auxiliary metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the design.The study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user understanding.The experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of explainability.Future work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.展开更多
Declaration of Competing Interest statements is updated in the published version of the following articles that appeared in issues of Resources Chemicals and Materials.The appropriate updated Declaration of Competing ...Declaration of Competing Interest statements is updated in the published version of the following articles that appeared in issues of Resources Chemicals and Materials.The appropriate updated Declaration of Competing Interest state-ments,provided by the Authors,are included below.展开更多
As vehicular networks become increasingly pervasive,enhancing connectivity and reliability has emerged as a critical objective.Among the enabling technologies for advanced wireless communication,particularly those tar...As vehicular networks become increasingly pervasive,enhancing connectivity and reliability has emerged as a critical objective.Among the enabling technologies for advanced wireless communication,particularly those targeting low latency and high reliability,time synchronization is critical,especially in vehicular networks.However,due to the inherent mobility of vehicular environments,consistently exchanging synchronization packets with a fixed base station or access point is challenging.This issue is further exacerbated in signal shadowed areas such as urban canyons,tunnels,or large-scale indoor hallswhere other technologies,such as global navigation satellite system(GNSS),are unavailable.One-way synchronization techniques offer a feasible approach under such transient connectivity conditions.One-way schemes still suffer from long convergence times to reach the required synchronization accuracy in these circumstances.In this paper,we propose a WLAN-based multi-stage clock synchronization scheme(WMC)tailored for vehicular networks.The proposed method comprises an initial hard update stage to rapidly achieve synchronization,followed by a high-precision stable stage based on Maximum Likelihood Estimation(MLE).By implementing the scheme directly at the network driver,we address key limitations of hard update mechanisms.Our approach significantly reduces the initial period to collect high-quality samples and offset estimation time to reach sub-50μs accuracy,and subsequently transitions to a refined MLE-based synchronization stage,achieving stable accuracy at approximately 30μs.The windowed moving average stabilized(reaching 90%of the baseline)in approximately 35 s,which corresponds to just 5.1%of the baseline time accuracy.Finally,the impact of synchronization performance on the localization model was validated using the Simulation of Urban Mobility(SUMO).The results demonstrate that more accurate conditions for position estimation can be supported,with an improvement about 38.5%in the mean error.展开更多
With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper propo...With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper proposes an improved fluid flow algorithm,aiming to enhance the computational efficiency of hydraulic fracturing simulations while ensuring computational accuracy.The algorithm optimizes the aperture law and iteration criteria,focusing on improving the domain volume and crack pressure update strategy,thereby enabling precise capture of dynamic borehole pressure variations during injection tests.The effectiveness of the algorithm is verified through three flow-solid coupling cases.The study also analyzes the effects of borehole size,domain volume,and crack pressure update strategy on fracturing behavior.Furthermore,the performance of the improved algorithm in terms of crack propagation rate,micro-crack formation,and fluid pressure distribution was further evaluated.The results indicate that while large-size boreholes delay crack initiation,the cracks propagate more rapidly once formed.Additionally,the optimized domain volume calculation and crack pressure update strategy significantly shorten the pressure propagation stage,promote crack propagation,and improve computational efficiency.展开更多
Colorectal cancer(CRC)is the most frequently diagnosed malignancy of the digestive system and the second leading cause of cancer-related deaths worldwide(1).In China,CRC ranks as the second most common cancer with inc...Colorectal cancer(CRC)is the most frequently diagnosed malignancy of the digestive system and the second leading cause of cancer-related deaths worldwide(1).In China,CRC ranks as the second most common cancer with incidence and mortality rates continuing to rise(2).The Chinese Society of Clinical Oncology(CSCO)first introduced its guidelines in 2017,and since then,they have been updated annually to incorporate the latest clinical research findings,drug availability,and expert consensus(3-8).This article presents the key updates in the 2025 edition compared to the 2024 version.展开更多
The Chinese Society of Clinical Oncology Non-small Cell Lung Cancer(CSCO NSCLC)guidelines were first published in 2016,ranking among the earliest-released guidelines within the CSCO series.In 2020 the CSCO published s...The Chinese Society of Clinical Oncology Non-small Cell Lung Cancer(CSCO NSCLC)guidelines were first published in 2016,ranking among the earliest-released guidelines within the CSCO series.In 2020 the CSCO published separate guidelines for NSCLC and small cell lung cancer(SCLC)for the first time to improve clinical usability.展开更多
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
The inspection of engine lubricating oil can give an indication of the internal condition of an engine. By means of the Object-Oriented Programming (OOP), an expert system is developed in this paper to computerize the...The inspection of engine lubricating oil can give an indication of the internal condition of an engine. By means of the Object-Oriented Programming (OOP), an expert system is developed in this paper to computerize the inspection. The traditional components of an expert system, such us knowledge base, inference engine and user interface are reconstructed and integrated, based on the Microsoft Foundation Class (MFC) library. To testify the expert system, an inspection example is given at the end of this paper.展开更多
This paper uses three size metrics,which are collectable during the design phase,to analyze the potentially confounding effect of class size on the associations between object-oriented（OO）metrics and maintainability...This paper uses three size metrics,which are collectable during the design phase,to analyze the potentially confounding effect of class size on the associations between object-oriented（OO）metrics and maintainability.To draw as many general conclusions as possible,the confounding effect of class size is analyzed on 127 C＋＋ systems and 113 Java systems.For each OO metric,the indirect effect that represents the distortion of the association caused by class size and its variance for individual systems is first computed.Then,a statistical meta-analysis technique is used to compute the average indirect effect over all the systems and to determine if it is significantly different from zero.The experimental results show that the confounding effects of class size on the associations between OO metrics and maintainability generally exist,regardless of whatever size metric is used.Therefore,empirical studies validating OO metrics on maintainability should consider class size as a confounding variable.展开更多
Expert systems (ESs) are being increasingly applied to the fault diagnosis of engines. Based on the idea of ES template (EST), an object-oriented rule-type EST is emphatically studied on such aspects as the object-ori...Expert systems (ESs) are being increasingly applied to the fault diagnosis of engines. Based on the idea of ES template (EST), an object-oriented rule-type EST is emphatically studied on such aspects as the object-oriented knowledge representation, the heuristic inference engine with an improved depth-first search (DFS) and the graphical user interface. A diagnositic ES instance for debris on magnetic chip detectors (MCDs) is then created with the EST. The spot running shows that the rule-type EST enhances the abilities of knowledge representation and heuristic inference, and breaks a new way for the rapid construction and implementation of ES.展开更多
A visual object-oriented software for lane following on intelligent highway system (IHS) is proposed. According to object-oriented theory, 3 typical user services of self-check, transfer of human driving and automatic...A visual object-oriented software for lane following on intelligent highway system (IHS) is proposed. According to object-oriented theory, 3 typical user services of self-check, transfer of human driving and automatic running and abnormal information input from the sensors are chosen out. In addition, the functions of real-time display, information exchanging interface, determination and operation interweaving in the 3 user services are separated into 5 object-oriented classes. Moreover, the 5 classes are organized in the visual development environment. At last, experimental result proves the validity and reliability of the control application.展开更多
Presents an object-oriented NBO(node-block-object)data model for hypermedia system.It takes advantage of object-oriented method,encapsulates all multimedia information as well as link functions in one unit,It has succ...Presents an object-oriented NBO(node-block-object)data model for hypermedia system.It takes advantage of object-oriented method,encapsulates all multimedia information as well as link functions in one unit,It has successfully achieved cross link to offer much better flexibility and two-way link to realize forward and backward searching in hypermedia system navigation.A conditional relation on links has also been realized,that is very helpful for time sensitive multimedia information processing and multimedia object cooperation.展开更多
基金supported by the National Key Research and Development Program of China under Grant 2022YFB2901501in part by the Science and Technology Innovation leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.
基金supported by the National Natural Science Foundation of China(Grant No.62362019)the Natural Science Foundation of Hainan Province(Grant No.624RC482)the Hainan Provincial Higher Education Teaching Reform Research Project(Grant Hnjg2024-27).
文摘In erasure-coded storage systems,updating data requires parity maintenance,which often leads to significant I/O amplification due to“write-after-read”operations.Furthermore,scattered parity placement increases disk seek overhead during repair,resulting in degraded system performance.To address these challenges,this paper proposes a Cognitive Update and Repair Method(CURM)that leverages machine learning to classify files into writeonly,read-only,and read-write categories,enabling tailored update and repair strategies.For write-only and read-write files,CURM employs a data-differencemechanism combined with fine-grained I/O scheduling to minimize redundant read operations and mitigate I/O amplification.For read-write files,CURM further reserves adjacent disk space near parity blocks,supporting parallel reads and reducing disk seek overhead during repair.We implement CURM in a prototype system,Cognitive Update and Repair File System(CURFS),and conduct extensive experiments using realworld Network File System(NFS)and Microsoft Research(MSR)workloads on a 25-node cluster.Experimental results demonstrate that CURMimproves data update throughput by up to 82.52%,reduces recovery time by up to 47.47%,and decreases long-term storage overhead by more than 15% compared to state-of-the-art methods including Full Logging(FL),ParityLogging(PL),ParityLoggingwithReservedspace(PLR),andPARIX.These results validate the effectiveness of CURM in enhancing both update and repair performance,providing a scalable and efficient solution for large-scale erasure-coded storage systems.
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00399401,Development of Quantum-Safe Infrastructure Migration and Quantum Security Verification Technologies).
文摘With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.
基金support of the Deanship of Research and Graduate Studies at Ajman University under Projects 2024-IRG-ENiT-36 and 2024-IRG-ENIT-29.
文摘Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction.Conventional design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design level.However,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such systems.This research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment analysis.The proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user context.The design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user context.Auxiliary metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the design.The study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user understanding.The experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of explainability.Future work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.
文摘Declaration of Competing Interest statements is updated in the published version of the following articles that appeared in issues of Resources Chemicals and Materials.The appropriate updated Declaration of Competing Interest state-ments,provided by the Authors,are included below.
基金supported by Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government(MOTIE)(No.20224B10300090)supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ITRC(Information Technology Research Center)support program(IITP-2025-RS-2021-II211835)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation).
文摘As vehicular networks become increasingly pervasive,enhancing connectivity and reliability has emerged as a critical objective.Among the enabling technologies for advanced wireless communication,particularly those targeting low latency and high reliability,time synchronization is critical,especially in vehicular networks.However,due to the inherent mobility of vehicular environments,consistently exchanging synchronization packets with a fixed base station or access point is challenging.This issue is further exacerbated in signal shadowed areas such as urban canyons,tunnels,or large-scale indoor hallswhere other technologies,such as global navigation satellite system(GNSS),are unavailable.One-way synchronization techniques offer a feasible approach under such transient connectivity conditions.One-way schemes still suffer from long convergence times to reach the required synchronization accuracy in these circumstances.In this paper,we propose a WLAN-based multi-stage clock synchronization scheme(WMC)tailored for vehicular networks.The proposed method comprises an initial hard update stage to rapidly achieve synchronization,followed by a high-precision stable stage based on Maximum Likelihood Estimation(MLE).By implementing the scheme directly at the network driver,we address key limitations of hard update mechanisms.Our approach significantly reduces the initial period to collect high-quality samples and offset estimation time to reach sub-50μs accuracy,and subsequently transitions to a refined MLE-based synchronization stage,achieving stable accuracy at approximately 30μs.The windowed moving average stabilized(reaching 90%of the baseline)in approximately 35 s,which corresponds to just 5.1%of the baseline time accuracy.Finally,the impact of synchronization performance on the localization model was validated using the Simulation of Urban Mobility(SUMO).The results demonstrate that more accurate conditions for position estimation can be supported,with an improvement about 38.5%in the mean error.
基金supported by the National Natural Science Foundation of China(Nos.52164001,52064006,52004072 and 52364004)the Science and Technology Support Project of Guizhou(Nos.[2020]4Y044,[2021]N404 and[2021]N511)+1 种基金the Guizhou Provincial Science and Technology Foundation(No.GCC[2022]005-1),Talents of Guizhou University(No.201901)the Special Research Funds of Guizhou University(Nos.201903,202011,and 202012).
文摘With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper proposes an improved fluid flow algorithm,aiming to enhance the computational efficiency of hydraulic fracturing simulations while ensuring computational accuracy.The algorithm optimizes the aperture law and iteration criteria,focusing on improving the domain volume and crack pressure update strategy,thereby enabling precise capture of dynamic borehole pressure variations during injection tests.The effectiveness of the algorithm is verified through three flow-solid coupling cases.The study also analyzes the effects of borehole size,domain volume,and crack pressure update strategy on fracturing behavior.Furthermore,the performance of the improved algorithm in terms of crack propagation rate,micro-crack formation,and fluid pressure distribution was further evaluated.The results indicate that while large-size boreholes delay crack initiation,the cracks propagate more rapidly once formed.Additionally,the optimized domain volume calculation and crack pressure update strategy significantly shorten the pressure propagation stage,promote crack propagation,and improve computational efficiency.
基金supported by the National Natural Science Foundation of China(No.82373415)Beijing Xisike Clinical Oncology Research Foundation(No.Ytongshu2021/ms-0003)。
文摘Colorectal cancer(CRC)is the most frequently diagnosed malignancy of the digestive system and the second leading cause of cancer-related deaths worldwide(1).In China,CRC ranks as the second most common cancer with incidence and mortality rates continuing to rise(2).The Chinese Society of Clinical Oncology(CSCO)first introduced its guidelines in 2017,and since then,they have been updated annually to incorporate the latest clinical research findings,drug availability,and expert consensus(3-8).This article presents the key updates in the 2025 edition compared to the 2024 version.
文摘The Chinese Society of Clinical Oncology Non-small Cell Lung Cancer(CSCO NSCLC)guidelines were first published in 2016,ranking among the earliest-released guidelines within the CSCO series.In 2020 the CSCO published separate guidelines for NSCLC and small cell lung cancer(SCLC)for the first time to improve clinical usability.
基金supported by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
文摘The inspection of engine lubricating oil can give an indication of the internal condition of an engine. By means of the Object-Oriented Programming (OOP), an expert system is developed in this paper to computerize the inspection. The traditional components of an expert system, such us knowledge base, inference engine and user interface are reconstructed and integrated, based on the Microsoft Foundation Class (MFC) library. To testify the expert system, an inspection example is given at the end of this paper.
基金The National Natural Science Foundation of China(No.60425206,60633010)
文摘This paper uses three size metrics,which are collectable during the design phase,to analyze the potentially confounding effect of class size on the associations between object-oriented（OO）metrics and maintainability.To draw as many general conclusions as possible,the confounding effect of class size is analyzed on 127 C＋＋ systems and 113 Java systems.For each OO metric,the indirect effect that represents the distortion of the association caused by class size and its variance for individual systems is first computed.Then,a statistical meta-analysis technique is used to compute the average indirect effect over all the systems and to determine if it is significantly different from zero.The experimental results show that the confounding effects of class size on the associations between OO metrics and maintainability generally exist,regardless of whatever size metric is used.Therefore,empirical studies validating OO metrics on maintainability should consider class size as a confounding variable.
文摘Expert systems (ESs) are being increasingly applied to the fault diagnosis of engines. Based on the idea of ES template (EST), an object-oriented rule-type EST is emphatically studied on such aspects as the object-oriented knowledge representation, the heuristic inference engine with an improved depth-first search (DFS) and the graphical user interface. A diagnositic ES instance for debris on magnetic chip detectors (MCDs) is then created with the EST. The spot running shows that the rule-type EST enhances the abilities of knowledge representation and heuristic inference, and breaks a new way for the rapid construction and implementation of ES.
文摘A visual object-oriented software for lane following on intelligent highway system (IHS) is proposed. According to object-oriented theory, 3 typical user services of self-check, transfer of human driving and automatic running and abnormal information input from the sensors are chosen out. In addition, the functions of real-time display, information exchanging interface, determination and operation interweaving in the 3 user services are separated into 5 object-oriented classes. Moreover, the 5 classes are organized in the visual development environment. At last, experimental result proves the validity and reliability of the control application.
文摘Presents an object-oriented NBO(node-block-object)data model for hypermedia system.It takes advantage of object-oriented method,encapsulates all multimedia information as well as link functions in one unit,It has successfully achieved cross link to offer much better flexibility and two-way link to realize forward and backward searching in hypermedia system navigation.A conditional relation on links has also been realized,that is very helpful for time sensitive multimedia information processing and multimedia object cooperation.
