Hot data identification is crucial for many applications though few investigations have examined the subject. All existing studies focus almost exclusively on frequency. However, effectively identifying hot data requi...Hot data identification is crucial for many applications though few investigations have examined the subject. All existing studies focus almost exclusively on frequency. However, effectively identifying hot data requires equally considering recency and frequency. Moreover, previous studies make hot data decisions at the data block level. Such a fine-grained decision fits particularly well for flash-based storage because its random access achieves performance comparable with its sequential access. However, hard disk drives （HDDs） have a significant performance disparity between sequential and random access. Therefore, unlike flash-based storage, exploiting asymmetric HDD access performance requires making a coarse-grained decision. This paper proposes a novel hot data identification scheme adopting multiple bloom filters to efficiently characterize recency as well as frequency. Consequently, it not only consumes 50% less memory and up to 58% less computational overhead, but also lowers false identification rates up to 65% compared with a state-of-the-art scheme. Moreover, we apply the scheme to a next generation HDD technology, i.e., Shingled Magnetic Recording （SMR）, to verify its effectiveness. For this, we design a new hot data identification based SMR drive with a coarse-grained decision. The experiments demonstrate the importance and benefits of accurate hot data identification, thereby improving the proposed SMR drive performance by up to 42%.展开更多
A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably m...A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.展开更多
A method of data processing to determine the coefficients of linearization equations for 1050 anemometer (produced by Thermo-Systems Inc. -TSI, USA) with the sensors made of domestic hot wire using the program preferr...A method of data processing to determine the coefficients of linearization equations for 1050 anemometer (produced by Thermo-Systems Inc. -TSI, USA) with the sensors made of domestic hot wire using the program preferred in this Paper is described. By calculation and test, it is indicated that the error resulting from this method is about 0. 5% of the full scale and less than TSl's. By using this method we can set up the calibration curve according to the measurement range and the diameter of the hot wire at a certain accuracy.展开更多
随着IT系统的广泛深入应用,其高可用性和容灾能力问题日益成为一个关注的焦点,双机热备及容灾系统的建设对于国防、政府、企业都有着重要的意义.鉴于磁盘阵列等技术未得到军方认证和使用者身份特殊的情况,提出了一种基于Oracle Data Gu...随着IT系统的广泛深入应用,其高可用性和容灾能力问题日益成为一个关注的焦点,双机热备及容灾系统的建设对于国防、政府、企业都有着重要的意义.鉴于磁盘阵列等技术未得到军方认证和使用者身份特殊的情况,提出了一种基于Oracle Data Guard技术的数据容灾备份策略,并在实际应用中取得了良好效果.展开更多
Alloying elements, such as silicon and manganese, have a major impact on the phase transformation point of steel. Specifically, manganese is an element for the expansion and stability of the austenite region, while si...Alloying elements, such as silicon and manganese, have a major impact on the phase transformation point of steel. Specifically, manganese is an element for the expansion and stability of the austenite region, while silicon can expand and stabilize the ferrite region. Phase transformation occurs during the hot rolling process for the steel with certain silicon content, which leads to great changes of the deformation resistance, thereby affecting the rolling stability. Consequently, a better understanding of phase transformation in the rolling process will contribute to the enhancement of product quality. In this paper ,the on-line rolling data were processed by means of the inverse calculation method. In this method, the steel deformation resistance with various silicon and manganese contents was obtained and analyzed to determine the deformation behavior of the steel, which can help improve the on-line control model and enhance the steel quality.展开更多
Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this ...Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this data offers. A free to use, scalable traffic crash hot spot detection tool was developed to aid law enforcement agency decision makers, statewide to the local municipality level. The tool was developed to identify crash hot spots algorithmically with </span><span style="font-family:Verdana;">a range of customizable parameters based on location, date and time, and</span><span style="font-family:Verdana;"> crash factors, enabling quick, dynamic queries. These capabilities provide the ability for law enforcement agencies to conduct “what if” analyses and make data-driven allocation decisions, placing officer resources where they are most needed. The two-step algorithm first identifies potential hot spots based on </span><span style="font-family:Verdana;">crash density and then ranks each hot spot using a standardized z-score </span><span style="font-family:Verdana;">measure of relative significance. To test the viability of the tool, a pilot was conducted identifying 27 hot spots across Wisconsin where targeted enforcement was then deployed. Despite officer skepticism, results from the pilot found officers at sites targeted for speeding and seatbelt violations were nearly twice as likely to initiate traffic stops compared to non-targeted hot spots. Empirical Bayes before-and-after crash analyses found fatal and injury crashes reduced significantly by nearly 11% during the months with targeted enforcement, while property damage crashes and total crashes were unchanged. Overall, the results show the algorithm can identify hotspots where, coupled with targeted enforcement, traffic safety improvements can be made.展开更多
基金This work was supported by Hankuk University of Foreign Studies Research Fund of Korea, and also partially supported by the National Science Foundation （NSF） Awards of USA under Grant Nos. 1053533, 1439622, 1217569, 1305237, and 1421913. Acknowledgment We would like to thank David Schwaderer （Samsung Semiconductor Inc., USA） for his valuable comments and proofreading.
文摘Hot data identification is crucial for many applications though few investigations have examined the subject. All existing studies focus almost exclusively on frequency. However, effectively identifying hot data requires equally considering recency and frequency. Moreover, previous studies make hot data decisions at the data block level. Such a fine-grained decision fits particularly well for flash-based storage because its random access achieves performance comparable with its sequential access. However, hard disk drives （HDDs） have a significant performance disparity between sequential and random access. Therefore, unlike flash-based storage, exploiting asymmetric HDD access performance requires making a coarse-grained decision. This paper proposes a novel hot data identification scheme adopting multiple bloom filters to efficiently characterize recency as well as frequency. Consequently, it not only consumes 50% less memory and up to 58% less computational overhead, but also lowers false identification rates up to 65% compared with a state-of-the-art scheme. Moreover, we apply the scheme to a next generation HDD technology, i.e., Shingled Magnetic Recording （SMR）, to verify its effectiveness. For this, we design a new hot data identification based SMR drive with a coarse-grained decision. The experiments demonstrate the importance and benefits of accurate hot data identification, thereby improving the proposed SMR drive performance by up to 42%.
文摘A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.
文摘A method of data processing to determine the coefficients of linearization equations for 1050 anemometer (produced by Thermo-Systems Inc. -TSI, USA) with the sensors made of domestic hot wire using the program preferred in this Paper is described. By calculation and test, it is indicated that the error resulting from this method is about 0. 5% of the full scale and less than TSl's. By using this method we can set up the calibration curve according to the measurement range and the diameter of the hot wire at a certain accuracy.
文摘Alloying elements, such as silicon and manganese, have a major impact on the phase transformation point of steel. Specifically, manganese is an element for the expansion and stability of the austenite region, while silicon can expand and stabilize the ferrite region. Phase transformation occurs during the hot rolling process for the steel with certain silicon content, which leads to great changes of the deformation resistance, thereby affecting the rolling stability. Consequently, a better understanding of phase transformation in the rolling process will contribute to the enhancement of product quality. In this paper ,the on-line rolling data were processed by means of the inverse calculation method. In this method, the steel deformation resistance with various silicon and manganese contents was obtained and analyzed to determine the deformation behavior of the steel, which can help improve the on-line control model and enhance the steel quality.
文摘Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this data offers. A free to use, scalable traffic crash hot spot detection tool was developed to aid law enforcement agency decision makers, statewide to the local municipality level. The tool was developed to identify crash hot spots algorithmically with </span><span style="font-family:Verdana;">a range of customizable parameters based on location, date and time, and</span><span style="font-family:Verdana;"> crash factors, enabling quick, dynamic queries. These capabilities provide the ability for law enforcement agencies to conduct “what if” analyses and make data-driven allocation decisions, placing officer resources where they are most needed. The two-step algorithm first identifies potential hot spots based on </span><span style="font-family:Verdana;">crash density and then ranks each hot spot using a standardized z-score </span><span style="font-family:Verdana;">measure of relative significance. To test the viability of the tool, a pilot was conducted identifying 27 hot spots across Wisconsin where targeted enforcement was then deployed. Despite officer skepticism, results from the pilot found officers at sites targeted for speeding and seatbelt violations were nearly twice as likely to initiate traffic stops compared to non-targeted hot spots. Empirical Bayes before-and-after crash analyses found fatal and injury crashes reduced significantly by nearly 11% during the months with targeted enforcement, while property damage crashes and total crashes were unchanged. Overall, the results show the algorithm can identify hotspots where, coupled with targeted enforcement, traffic safety improvements can be made.
