• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.11-19
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• 2022

In this paper, we propose a fault tolerant embedded system using memory redundancy on the full-virtualization based virtual machine monitor. The proposed virtual machine monitor first virtualizes main memory of embedded system utilizing efficient shadow page table scheme so that the embedded system runs as a virtual machine on the virtual machine monitor. The virtual machine monitor makes the backup of the embedded system run as another virtual machine by copying memory contents of the embedded system into memory space of backup system according to predefined schedules. When an error occurs in the target virtual machine, the corresponding standby virtual machine takes the role of target virtual machine and continues its operation. Performance evaluation studies show that such backups and switches of virtual machines are performed with minor performance degradation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5234-5251
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• 2018

It is well-known that virtualization technology can bring many benefits not only to users but also to service providers. From the view of system security and resource utility, higher resource sharing degree and higher system reliability can be obtained by the introduction of virtualization technology in distributed cloud. The small size time-sharing multiplexing technology which is based on virtual machine in distributed cloud platform can enhance the resource utilization effectively by server consolidation. In this paper, the concept of memory block and user satisfaction is redefined combined with user requirements. According to the unbalanced memory resource states and user preference requirements in multi-virtual machine environments, a model of proper memory resource allocation is proposed combined with memory block and user satisfaction, and at the same time a memory optimization allocation algorithm is proposed which is based on virtual memory block, makespan and user satisfaction under the premise of an orderly physical nodes states also. In the algorithm, a memory optimal problem can be transformed into a resource workload balance problem. All the virtual machine tasks are simulated in Cloudsim platform. And the experimental results show that the problem of virtual machine memory resource allocation can be solved flexibly and efficiently.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.201-203
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• 2005

The best method to study language is to talking with a native speaker. A voice recognition technology can be used to develope a language study machine. SD(Speaker dependant) and SI(speaker independant) voice recognition method is used for the language study machine. MP3 Player. FM Radio. Alarm clock functions are added to enhance the value of the product. The machine is designed with a DSP(Digital Signal Processing) chip for voice recognition. MP3 encoder/decoder chip. FM tumer and SD flash memory card. This paper deals with the application of SD ad SD voice recognition. flash memory file system. PC download function using USB ports, English conversation text function by the use of SD flash memory. LCD display control. MP3 encoding and decoding, etc. The study contents are saved in SD flash memory. This machine can be helpful from child to adult by changing the SD flash memory.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.8
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• pp.378-384
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• 2008

Many embedded systems are supporting Java as their software platform via Java virtual machine. Java virtual machine manages memory automatically by providing automatic memory management, i.e. garbage collector. Because only scarce memory is available to embedded system, Java virtual machine should use small memory and manage it efficiently. This paper introduces two memory management techniques to exploit small memory in Java virtual machine which can execute multiple Java applications concurrently. First, compaction based garbage collection is introduced to overcome external fragmentation problem in presence of immovable memory area. Then garbage collector driven class unloading is introduced to reduce memory use of unnecessary loaded classes. We implemented these techniques in working embedded system and observed that they are very efficient, since more Java applications are able to be executed concurrently and memory use is also reduced with these techniques.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1440-1444
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• 2011

Memory-device is one of the auxiliary components of point-switch machine; connecting front-rod and tongue-rail. The right side and left side of memory-device are different from each other. When there would be a derailing accident of rolling stock or motor-car, the memory-device properly bends and protects the internal of point-switch machine. Memory-device is one of the important site maintenance spare parts. Memory-device for each of right and left side should be secured so that they can be installed on correct side during an exchange work. This study suggests the development of memory-device with different left and right side and the performance test of it. The study intends to contribute in the convenience improvement of maintenance by improving the unification of memory-device.

• Journal of Platform Technology
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• v.11 no.4
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• pp.71-79
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• 2023

In this paper, we analyze the characteristics of machine learning workloads and, based on them, propose a distributed in-memory caching technique to improve the performance of machine learning workloads. The core of machine learning workload is model training, and model training is a computationally intensive task. Performing machine learning workloads in a Kubernetes-based cloud environment in which the computing framework and storage are separated can effectively allocate resources, but delays can occur because IO must be performed through network communication. In this paper, we propose a distributed in-memory caching technique to improve the performance of machine learning workloads performed in such an environment. In particular, we propose a new method of precaching data required for machine learning workloads into the distributed in-memory cache by considering Kubflow pipelines, a Kubernetes-based machine learning pipeline management tool.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1335-1338
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• 2003

Fixed-size memory allocation is one of the most promising way to avoid external fragmentation in dynamic memory allocation problem. This paper presents an experimental result of applying the fixed- size memory allocation strategy to Java virtual machine for embedded system. The result says that although this strategy induces another memory utilization problem caused by internal fragmentation, the effect is not very considerable and this strategy is well-suited for embedded Java system. The experiment has been performed in a real embedded Java system called the simpleRTJ.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.615-619
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• 2021

This study focuses on memory systems that are optimized to increase performance and energy efficiency in many embedded systems such as IoT, cloud computing, and edge computing, and proposes a performance improvement technique. The proposed technique improves memory system performance based on machine learning algorithms that are widely used in many applications. The machine learning technique can be used for various applications through supervised learning, and can be applied to a data classification task used in improving memory system performance. Data classification based on highly accurate machine learning techniques enables data to be appropriately arranged according to data usage patterns, thereby improving overall system performance.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.39-43
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• 2017

As the industry grows, the amount of data grows exponentially, and data analysis using these serves as a predictable solution. As data size increases and processing speed increases, it has begun to be applied to new fields by combining artificial intelligence technology as well as simple big data analysis. In this paper, we propose a method to quickly apply a machine learning based algorithm through efficient resource allocation. The proposed algorithm allocates memory for each attribute. Learning Distinct of Attribute and allocating the right memory. In order to compare the performance of the proposed algorithm, we compared it with the existing K-means algorithm. As a result of measuring the execution time, the speed was improved.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.5
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• pp.462-468
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• 2009

In this study, a workpiece-chucking device that generates a chucking force from a shape memory alloy is introduced. This paper first presents train procedure to transform a commercial one-way shape memory alloy into a two-way shape memory alloy, which makes unclamping mechanism of the chucking device simpler than that using the one-way shape memory alloy Second, it describes a conceptual design of the workpiece-chucking device using the two-way type shape memory alloy. Third, it presents a prototype and its chucking characteristics, such as time-response of clamping/unclamping operations and a relationship between temperatures and chucking forces. Finally, it describes a mill-machining test conducted with the prototype. The results confirm that the proposed workpiece-chucking device is feasible for micro machine-tools.