4-2: 네거티브 디자인 개념을 활용한 군함 초기 설계에 적용되는 디자인 영역 탐색
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The document discusses using negative space in figure drawing to help draw the figure more easily. It then discusses using negative design space exploration in early stage naval ship design rather than positive design. This allows removing constraints to identify uncertainties and define feasible design ranges earlier to support decision making. Dynamic visualization tools are proposed to allow quicker exploration of design space, generation of more design alternatives, and obtaining insights from sensitivity analysis earlier in the design process.
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Final Presentation
Ash Abel completed a summer internship with Clemson University's Mechanical Engineering department. Their main project involved vibration and acoustic testing of a 7.5MW test bench that emitted uncomfortable noise levels. Through research, testing, and analysis, Ash identified possible causes such as loose connections and resonance. They designed tests using accelerometers and microphones to analyze vibration and acoustic data. Potential solutions like shortening the support beam or changing its cross-section were modeled and tested. Analysis showed modifying connections and the beam's design could effectively reduce noise levels. Ash also assisted engineers and created support materials during their internship.
ISSCS2011
This document proposes using CELP-based features for environmental sound recognition (ESR). Conventional features like MFCCs do not perform well for ESR due to unstructured sounds. CELP features like linear prediction coefficients, pitch, gain, and codebook pulses capture sound properties better. Experimental results show CELP and CELP+MFCC outperform MFCC for ESR by 10%, with CELP providing low complexity and real-time extraction. CELP features are more suitable than MFCCs for ESR due to capturing long and short-term prediction in an unstructured setting while maintaining simplicity.
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Model compression
Model Compression (NanheeKim)
@NanheeKim @nh9k
질문이 있으면 언제든지 연락주세요!
공부한 것을 바탕으로 작성한 ppt입니다.
출처는 슬라이드 마지막에 있습니다!
Please, feel free to contact me, if you have any questions!
github: https://github.com/nh9k
email: kimnanhee97@gmail.com
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
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Qualitative and quantitative comparative human articulation reconstruction precision evaluations against the state-of-the-art strategies have also been carried out.
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Ash Abel completed a summer internship with Clemson University's Mechanical Engineering department. Their main project involved vibration and acoustic testing of a 7.5MW test bench that emitted uncomfortable noise levels. Through research, testing, and analysis, Ash identified possible causes such as loose connections and resonance. They designed tests using accelerometers and microphones to analyze vibration and acoustic data. Potential solutions like shortening the support beam or changing its cross-section were modeled and tested. Analysis showed modifying connections and the beam's design could effectively reduce noise levels. Ash also assisted engineers and created support materials during their internship.
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Model compression
Model Compression (NanheeKim)
@NanheeKim @nh9k
질문이 있으면 언제든지 연락주세요!
공부한 것을 바탕으로 작성한 ppt입니다.
출처는 슬라이드 마지막에 있습니다!
Please, feel free to contact me, if you have any questions!
github: https://github.com/nh9k
email: kimnanhee97@gmail.com
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Watch the video presentation: http://wp.me/p3RLHQ-ghU
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Talk Abstract
As with all open-source databases, Accumulo developers often compete between building exciting new features and hacking on performance and stability. As the core features solidify and expand, we see many opportunities to improve performance. An effective methodology for performance improvement is scientific in nature, and follows a well-definite modeling and simulation approach, matching theory to experimentation in an iterative fashion.
Ingest performance is one of the most differentiating characteristics of Accumulo. However, there is much room for improvement for typical ingest-heavy applications. Accumulo supports two mechanisms to bring data in: streaming ingest and bulk ingest. In bulk ingest, the goal is to maximize throughput without constraining latency. Bulk ingest involves creating a set of files that conform to Accumulo's internal RFile format and then registering those files with Accumulo. MapReduce provides a framework for generating, sorting, and storing key/value pairs, which form the primary elements of preparing RFiles for bulk ingest. MapReduce has been used many times over the years to break sorting records, such as Terasort. We can expect it is a reasonable choice for maximizing bulk ingest throughput. However, the theory often proves challenging to implement as there are many performance pitfalls along the way.
In this talk, we dive deep into optimizing MapReduce for Accumulo bulk ingest. We share detailed theoretical and empirical performance models, we discuss techniques for profiling performance, and we suggest reusable techniques for squeezing the maximum performance out of enterprise-grade Accumulo bulk ingest.
Speaker
Chris McCubbin
Director of Data Science, Sqrrl
Chris is the Director of Data Science for Sqrrl. He has extensive experience with the Hadoop ecosystem and applying scientific computation algorithms to real-world datasets. Previously, Chris developed Big Data analysis tools for the Intelligence Community and applied artificial intelligence techniques to unmanned vehicle systems. He holds a MS in Computer Science and BS in Computer Science and Mathematics from the University of Maryland.
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Integrated into Intel® Advisor, Cache-aware Roofline Modeling (CARM) provides insight into how an application behaves by helping to determine a) how optimally it works on a given hardware, b) the main factors that limit performance, c) if the workload is memory or compute-bound, and d) the right strategy to improve application performance.
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In this video, Dr. Umit Catalyurek from Georgia Institute of Technology presents: Modern Computing: Cloud, Distributed, & High Performance.
Ümit V. Çatalyürek is a Professor in the School of Computational Science and Engineering in the College of Computing at the Georgia Institute of Technology. He received his Ph.D. in 2000 from Bilkent University. He is a recipient of an NSF CAREER award and is the primary investigator of several awards from the Department of Energy, the National Institute of Health, and the National Science Foundation. He currently serves as an Associate Editor for Parallel Computing, and as an editorial board member for IEEE Transactions on Parallel and Distributed Computing, and the Journal of Parallel and Distributed Computing.
Learn more: http://www.bigdatau.org/data-science-seminars
Watch the video presentation: http://wp.me/p3RLHQ-ghU
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Lec9 Computer Architecture by Hsien-Hsin Sean Lee Georgia Tech -- Memory part 1
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This document summarizes key topics about memory hierarchy design, including:
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The document proposes a hybrid approach to scalably computing pairwise comparisons of large datasets that follows a Zipf-Mandelbrot distribution. It involves pre-computing and storing the most frequent "head" comparisons while computing less frequent "body" and trivial "tail" comparisons on-demand. This balances storage and computation needs while allowing dimensionality reduction as the dataset scales. Preliminary experiments tested the approach on loan data using HBase and HDFS. Further performance testing and integration with Hadoop is suggested to prove the approach.
EOS5 Demo
The document proposes a hybrid approach to scalably computing pairwise comparisons of large datasets that follows a Zipf-Mandelbrot distribution. It involves pre-computing and storing the most frequent "head" comparisons while computing less frequent "body" and trivial "tail" comparisons on-demand. This balances storage and computation needs while allowing dimensionality reduction as the dataset scales. Preliminary experiments tested the approach on loan data using HBase and HDFS. Further performance testing and integration with Hadoop is suggested to prove the approach.
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Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
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- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
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官方认证美国密歇根州立大学毕业证学位证书原版一模一样
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
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Understanding Inductive Bias in Machine Learning
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Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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A review on techniques and modelling methodologies used for checking electrom...
A review on techniques and modelling methodologies used for checking electrom...
4-2: 네거티브 디자인 개념을 활용한 군함 초기 설계에 적용되는 디자인 영역 탐색
- 2. ① Feasibility Study ② Concept Design ③ Preliminary Design ④ Contract Design ⑤ Detail Design
- 3. • Impossible of creative designs in the early-stage naval ship design due to the constraint of flexibility and affordability occurred by specified requirements • More possibility of requirement chances by the need of rapidly evolved technologies and complexities • Need quicker and more statistical data at the early stage of naval ship design Design Space
- 4. “Negative Space” is what we call the space surrounding the figure. By placing areas of negative spaces as accurately as you can in relationship to the whole, your drawing of the figure will come easier. http://lifedrawingposes.com/life-drawing-figure-drawing-tips/life-drawing-techniques-negative-space/ Positive Space Negative Space Positive Space Negative Space
- 5. 104 pixels 1,664 pixels 416 pixels 106,496 pixels 26,624 pixels * With permission of the use of figures from Professor McKesson at U.N.O 6,656 pixels
- 6. 106,496 pixels 104 pixels • Quicker early stage design space exploration compared to positive design • Remove the possibility of over/under-optimization at the early stage design • Identify uncertainties thus removing technical risks earlier • Define feasible design range quicker and support decision making with information * With permission of the use of figures from Professor McKesson at U.N.O
- 8. Point Design Approach : conventional Family of Design Approach : New Few design alternatives manually generated More design alternatives Time-consuming nature Time-saving nature A small amount of information Maximum amount of information Local Optimization Global Optimization
- 11. C.I L(m) B(m) T(m) Full disp. (MTon) Max speed (kts) Power (hp) TF Fn_vol Hull form Propulsor Lower 25% 26.1 5.8 1.3 78.8 38.5 5,267 5.9 5.0 Semi- planning : 63% Planning : 37% W/J : 50% FPP : 50% Median 31.7 6.7 1.5 109.0 40.5 7,396 6.9 5.8 Upper 75% 44.3 7.2 2.4 236.3 45.0 12,000 7.7 6.7
- 13. L ∼ f(T, Full Disp., Power) B ∼ f(Full Disp.) T ∼ f(L, Full Disp., Power) Full Disp. ∼ f(B, T, Power) Max Speed ∼ weak f(B, Full Disp.) Power ∼ f(L, T, Full Disp.) • Obtain insightful information for Sensitivity Analysis
- 14. alternative 1 alternative 2 alternative 3 Outer profile Hull-form planning semi-planning semi-planning Propulsor WaterJet WaterJet FPP max speed. 40 ~ 55 kts 30 ~ 45 kts Length overall 26.1~44.3m Breadth max. 5.8 ~ 7.2m T 1.3 ~ 2.4m full load 78.8~236.3tom
- 17. Factor Design Response L 36.99m Max Speed 40kts B 5.95m Full Disp. 200ton T 1.66m Power 10,810hp Hull Form Planing Propulsor WaterJet
- 20. • Find design patterns by distributions and correlations, and predict the performance by the regression analysis • Establish deterministic models at the early-stage design • Based on the identifying of infeasible design spaces, more design information can be obtained. • Provide dynamic information visualization for requirement confirmation and refinement • Provide simple way to conduct sensitivity analysis of factors and responses in a simple manner