• Journal of Korea Multimedia Society
• /
• v.13 no.11
• /
• pp.1643-1656
• /
• 2010

It is necessary to develop a system to judge inferiority of goods to minimize the loss at small factories in which produces various kinds of goods with small amounts. That system can be developed based on HTM theory. HTM is a model to apply the operation principles of the neocortex in human brain to the machine learning. We have to build the trained HTM network to use the HTM-based machine learning system. It requires the knowledge for the HTM theory. This paper presents the design and implementation of the training system to support the development of HTM networks which recognize the molding parts to judge its badness. This training system allows field technicians to train the HTM network with high accuracy without the knowledge of the HTM theory. It also can be applied to any kind of the HTM-based judging systems for molding parts.

• Proceedings of the Korea Contents Association Conference
• /
• 2017.05a
• /
• pp.29-30
• /
• 2017

최근 Hardware Transaction Memory (HTM)으로 트랜잭션을 처리하는 기술이 각광을 받고 있다. 그러나 HTM의 처리 실패 요인 중 하나는 캐시를 사용하여 트랜잭션을 처리하여 용량에 제한이 존재한다. 이러한 이유로 long 트랜잭션의 경우 용량을 초과하여 처리가 불가능한 경우가 빈번히 발생한다. 이를 해결하기 위해 본 논문에서는 long 트랜잭션 처리를 위한 HTM 기반 트랜잭션 분할 기법을 제안한다. 제안하는 기법은 먼저 HTM 으로 수행하여 캐시 용량을 초과하는 경우, long 트랜잭션을 다수의 트랜잭션으로 분할한다. 분할된 트랜잭션이 수행이 완료되면, 부분 커밋(commit)을 수행하고 이에 대한 정확성을 제공하기 위해 validation을 수행한다. 분할된 모든 트랜잭션의 수행이 완료되면 최종적인 커밋을 수행한다. 이를 통하여 기존 HTM 으로 처리하기 불가능한 long 트랜잭션을 속도가 우수한 HTM을 기반으로 효율적인 트랜잭션 처리가 가능하다.

• Journal of Korea Multimedia Society
• /
• v.13 no.10
• /
• pp.1494-1505
• /
• 2010

A variety of sounds take place in medium and small-sized manufactories producing many kinds of parts in a small quantity with one press. We developed the identification system for the quality of parts using HTM(Hierarchical Temporal Memory)-based sound recognition. HTM is the theory that the operation principle of human brain's neocortex is applied to computer, suggested by Jeff Hopkins. This theory memorizes temporal and spatial patterns hierarchically about the real world, which is known for its cognitive power superior to the previous recognition technologies in many cases. By applying the HTM model to the sound recognition, we developed the identification system for the quality of molding parts. In order to verify its performance we recorded the various sounds at the moment of producing parts in the real factory, constructed the HTM network of sound, and then identified the quality of parts by repeating learning and training. It reveals that this system gets an excellent and accurate results at the noisy factory.

• Bulletin of the Korean Chemical Society
• /
• v.33 no.2
• /
• pp.670-674
• /
• 2012

The photovoltaic performance of solid-state dye-sensitized solar cells employing hole transport material (HTM), 2,2',7,7'-tetrakis-(N,N-di-p-methoxyphenyl-amine)-9,9'-spirobifluorene (spiro-MeOTAD), has been investigated in terms of HTM overlayer thickness. Two important parameters, soak time and spin-coating rate, are varied to control the HTM thickness. Decrease in the period of loading the spiro-MeOTAD solution on $TiO_2$ layer (soak time) leads to decrease in the HTM overlayer thickness, whereas decrease in spin-coating rate increases the HTM overlayer thickness. Photocurrent density and fill factor increase with decreasing the overlayer thickness, whereas open-circuit voltage remains almost unchanged. The improved photocurrent density is mainly ascribed to the enhanced charge transport rate, associated with the improved charge collection efficiency. Among the studied HTM overlayer thicknesses, ca. 230 nm-thick HTM overlayer demonstrates best efficiency of 4.5% at AM 1.5G one sun light intensity.

• KIISE Transactions on Computing Practices
• /
• v.23 no.8
• /
• pp.466-472
• /
• 2017

Hardware Transactional Memory (HTM) has greatly changed the parallel programming paradigm for transaction processing. Since Intel has recently proposed Transactional Synchronization Extension (TSX), a number of studies based on HTM have been conducted. However, the existing studies support conflict prediction for a single cause of the transaction processing and provide a standardized TSX environment for all workloads. To solve the problems, we propose an efficient hardware transactional memory scheme for processing transactions in multi-core in-memory environment. First, the proposed scheme determines whether to use Software Transactional Memory (STM) or the serial execution as a fallback path of HTM by using a prediction matrix to collect the information of previously executed transactions. Second, the proposed scheme performs efficient transaction processing according to the characteristic of a given workload by providing a retry policy based on machine learning algorithms. Finally, through the experimental performance evaluation using Stanford transactional applications for multi-processing (STAMP), the proposed scheme shows 10~20% better performance than the existing schemes.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.13 no.2
• /
• pp.49-61
• /
• 2017

Transactional Memory (TM) has greatly changed the parallel programming paradigm for transaction processing and is classified into STM, HTM, HyTM according to hardware or software frameworks. However, the existing studies have a problem that they provide static retry policy for all workloads. To solve the problems, we propose an hybrid transactional memory scheme using sampling-based adaptive retry policy in multi-core environment. First, the proposed scheme determines whether to use STM or HTM according to the characteristic of a transaction. Otherwise, it executes HTM and STM concurrently by using a bloom filter. Second, the proposed scheme provides adaptive retry policy for HTM according to the characteristic of transactions in each workload. Finally, through the experimental performance evaluation using STAMP, the proposed scheme shows 10~20% better performance than the existing schemes.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2016.02a
• /
• pp.415.1-415.1
• /
• 2016

2009년도에 Perovskite가 태양전지에 처음 적용된 이후, Perovskite를 기반으로 하는 태양전지는 급속한 발전을 이루고 있으나, 향후 상용화를 위해서는 추가적인 공정개선 및 제조 단가를 낮추는 노력이 필수적이다. 초창기 Perovskite의 증착 공정은 One step deposition 방법이 사용되었으나, Layer의 thickness, uniformity 등을 조절하기 어려워 Sequential deposition 방법으로 개선되었다. 하지만 결과적으로 초기방법 대비 추가공정이 발생함에 따라 시간 및 비용의 증가가 불가피하였다. 제조단가 측면에서는 Perovskite 태양전지를 구성하는 재료 중 HTM(정공수송물질)을 구성하는 Spiro-MeOTAD의 비용이 가장 비싸다. 따라서 저비용 태양전지를 위해서는 HTM이 없는 구조가 필요하다. 이 페이퍼에서는 Perovskite 물질이 고흡광 능력 외에 충분한 전하수송능력을 보유한다는 점에 착안하여, Gas Pressure Assisted Modified One Step Deposition을 이용한 HTM Free Perovskite를 제작하고 기존의 Sequential Deposition Method 통해 만들어진 Perovskite 태양전지와 비교/분석하였다.

• Journal of the Korean Society of Visualization
• /
• v.13 no.3
• /
• pp.15-19
• /
• 2015

We developed a high-throughput microscopy (HTM) method which enabled us to replace a conventional phase contrast microscopy (PCM) method that has been used as a standard analytical method for airborne asbestos. We could obtain the concentration of airborne asbestos fibers under detection limit by automated image processing and analysis using HTM method. Here we propose an improved image processing algorithm with variable parameters to enhance the accuracy of the HTM analysis. Since the variable parameters that compensate the difference of the brightness are applied to the individual images in our new image processing method, it is possible to enhance the accuracy of the automatic image analysis method for sample slides with low asbestos concentration that caused errors in binary image processing. We demonstrated that enumeration of fibers by improved image processing algorithm remarkably enhanced the accuracy of HTM analysis in comparison with PCM. The improved HTM method can be a potential alternative to conventional PCM.

• Journal of Korea Game Society
• /
• v.20 no.6
• /
• pp.13-22
• /
• 2020

RIO is a new network API for Windows that is designed to have high I/O performance through low overhead and latency. Using RIO, MMO game servers may have much performance benefits. In addition, HTM has better productivity and performance compared to existing synchronization methods, so adopting it may produce better performance, also. In this paper, we improved server performance by implementing a new MMO game server architecture optimized with RIO and HTM. The performance of the server was verified through a benchmark program, and the number of concurrent users increased by 19%.

• Journal of Korea Multimedia Society
• /
• v.14 no.9
• /
• pp.1152-1164
• /
• 2011

Stock price is stream data to change continuously. The characteristics of these data, stock trends according to flow of time intervals may differ. therefore, stock price should be continuously prediction when the price is updated. In this paper, we propose the new prediction system that continuously predicts the stock price according to the predefined time intervals for the selected stock item using HTM model. We first present a preprocessor which normalizes the stock data and passes its result to the stream sensor. We next present a stream sensor which efficiently processes the continuous input. In addition, we devise a storage node which stores the prediction results for each level and passes it to next upper level and present the HTM network for prediction using these nodes. We show experimented our system using the actual stock price and shows its performance.