Optimal implementations of UPGMA and other common clustering algorithms

In this work we consider hierarchical clustering algorithms, such as UPGMA, which follow the closest-pair joining scheme. We survey optimal O(n²)-time implementations of such algorithms which use a ‘locally closest’ joining scheme, and specify conditions under which this relaxed joining scheme is equivalent to the original one (i.e. ‘globally closest’).     

CNC机床结构动态设计方法研究

提出机床结构动态设计基本原则。以某数控弧齿铣齿机为研究对象,应用相关的动态设计原则,通过变量化设计与有限元分析,对该机床进行结构动态设计。结果表明,应用该方法设计的机床结构具有良好的动静态特性。     

数控机床交流伺服系统的复合控制研究

针对常规P-P与PPI控制存在的问题，提出了在数控机床交流伺服系统中，采用带速度和加速度作为前馈控制，模糊自校正PID控制作为位置反馈控制的复合控制器，实验证明可以显著提高控制系统的控制精度，大大降低跟踪误差，具有较强的鲁棒性。     

提高E^2PROM中NMOS管源漏穿通电压的实验研究

为了提高Ｅ＾２ＰＲＯＭ中Ｎ管源漏穿通电压，用实验的方法对制造工艺进行了研究。结果表明，高能量注入是提高ＶＰＴ的有效手段，但受到ｐｎ结击穿的限制，只适用于低区短沟Ｎ管；ＤＤＤ工艺大幅度高ＶＰＴ，但ｐｎ结击穿电压低于２０Ｖ，不能应用于高压ＭＯＳ管；采用适量的防穿通注入和适当增大沟道长度为最理想的工艺途径。     

Locational analysis for regionalization of Turkish Red Crescent blood services

After a series of earthquakes in 1999, Turkish Red Crescent (TRC) has engaged in a restructuring for all of its activities, including the blood services. Our study on the blood management system had been started as part of this initiative to restructure the blood services and improve both their effectiveness and efficiency. In the current system of TRC, not much consideration has been given to how the locational decisions affect the performance of blood centers, stations and mobile units. In recent years, however, there has been much discussion regarding the regionalization of the blood management system in Turkey. In this study, we develop several mathematical models to solve the location–allocation decision problems in regionalization of blood services. We report our computational results, obtained by using real data, for TRC blood services.     

Parallel-access memory management using fast-fits

The two most common approaches to managing shared-access memory-free lists and buddy systems-have significant drawbacks. Free list algorithms have poor memory access characteristics, and buddy systems utilize their space inefficiently. In this paper, we present an alternative approach to parallel-access memory management based on the fast-fits algorithm. A fast-fits memory manager stores free blocks in a tree structure, providing fast access and efficient space use. Since the fast-fits algorithm accesses fewer blocks than a free list algorithm, it reduces the amount of cache invalidation overhead due to the memory manager. Our performance experiments show that the parallel-access fast-fits memory manager allows significantly greater access rates than a serial-access fast-fits memory manager does. We note that shared-memory multiprocessor systems need efficient dynamic storage allocators, both for system purposes and to support parallel programs.     

从萨沃伊别墅探究勒·柯布西耶的机器美学

文章论述了萨沃伊别墅的机器美学思想,主要结合了勒·柯布西耶在1926年提出的"建筑新五点"来探讨他所主张的机器美学在萨沃伊别墅上是如何得以实现。     

台达机电产品在高速集束球经机中的应用

本文主要介绍台达20PM运动控制器在纺织行业的高速集束球经机中的应用。阐述了球经机的技术特点和工艺要求,简介了控制系统的硬件配置和应用程序。在控制系统中利用20PM内置的多组电子凸轮功能实现了两边成型和精密绕排等各项工艺目标。     

LPNet: A DNN based latency prediction technique for application mapping in Network-on-Chip design

Analytical models used for latency estimation of Network-on-Chip (NoC) are not producing reliable accuracy. This makes these analytical models difficult to use in optimization of design space exploration. In this paper, we propose a learning based model using deep neural network (DNN) for latency predictions. Input features for DNN model are collected from analytical model as well as from Booksim simulator. Then this DNN model has been adopted in mapping optimization loop for predicting the best mapping of given application and NoC parameters combination. Our simulations show that using the proposed DNN model, prediction error is less than 12% for both synthetic and application specific traffic. More than 108 times speedup could be achieved using DPSO with DNN model compared to DPSO using Booksim simulator.     

Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating with various lighting conditions. Classifying networks were given a single image or multiple images of various lighting conditions for training and testing. CNNs demonstrated significantly better performance than NNs across all tasks. Furthermore, CNNs demonstrated improved generalizability, i.e., the ability to generalize to more diverse data than either the training or validation data sets. Specifically, CNNs trained on high-resolution layerwise images from one build showed minimal loss in performance when applied to data from an independent build, whereas the performance of the NNs degraded significantly. CNN accuracy was also demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate. CNNs demonstrated accuracies of 93.5 % on large (>200 μm) flaws when testing and training on components from the same build and accuracies of 87.3 % when testing on a previously unseen build. Finally, evidence linking the formation of large lack-of-fusion defects to the presence of process ejecta is presented.