• Journal of Digital Contents Society
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• v.13 no.1
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• pp.75-81
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• 2012

SSDs(Solid State Drives) have many attractive features such as high performance, low power consumption, shock resistance, and low weight, so they replace HDDs to a certain extent. An SSD has FTL(Flash Translation Layer) which emulate block storage devices like HDDs. A garbage collection, one of major functions of FTL, effects highly on the performance and the lifetime of SSDs. However, there is no de facto standard for new garbage collection algorithms. To solve this problem, we propose trace driven offline optimal algorithms for garbage collection of FTL. The proposed algorithm always guarantees minimal number of erase operation. In addition, we verify our proposed algorithm using TPC trace.

• Journal of the Korea Society of Computer and Information
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• v.22 no.4
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• pp.25-32
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• 2017

Recently, the use of NAND flash memory is being increased as a secondary device to displace conventional magnetic disk. NAND flash memory, as one among non-volatile memories, has many advantages such as low power, high reliability, low access latency, and so on. However, NAND flash memory has disadvantages such as erase-before-write, unbalanced operation speed, and limited P/E cycles, unlike conventional magnetic disk. To solve these problems, NAND flash memory mainly adopted FTL (Flash Translation Layer). In particular, garbage collection technique in FTL tried to improve the system lifetime. However, previous garbage collection techniques have a sensitive property of the system lifetime according to write pattern. To solve this problem, we propose BSGC (Balanced Selection-based Garbage Collection) technique. BSGC efficiently selects a victim block using all intervals from the past information to the current information. In this work, SFL (Search First linked List), as the proposed block allocation policy, prolongs the system lifetime additionally. In our experiments, SFL and BSGC prolonged the system lifetime about 12.85% on average and reduced page migrations about 22.12% on average. Moreover, SFL and BSGC reduced the average response time of 16.88% on average.

• Journal of Institute of Convergence Technology
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• v.2 no.2
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• pp.13-19
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• 2012

This Design research is a design of rescue equipment combined with beach garbage can for prompt rescue at the time of the accident in water. As a usual, it is used as garbage can hanging a garbage bag on the holders located in both sides, and it can be used to rescue drowning men promptly taking out a rescue tube located inside of the equipment at the time of the accident in water. Support is consisted of beach one, wall-hanging one and pipe one, and it could be installed to match the situation because it can be combined with all main frames. As a usual, it is used as garbage can in seaside, but it can be used to rescue drowning men promptly taking out a rescue tube after pulling the handle located in front of main frame at the time of the accident in water, and it can be used to pull drowning men out of the water because rope is kept in rescue tube.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.3
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• pp.149-157
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• 2017

In this paper, we propose BCMR (Block Classification with Monitor and Restriction) to ensure the isolation and to reduce the interference of blocks between a garbage collection and a wear leveling. The proposed BCMR monitors an endurance variation of blocks during the garbage collection and detects hot blocks by making a restriction condition based on this information. The proposal induces a block classification by its update frequency for the garbage collection and the wear leveling, so we will get a prolonged lifetime of NAND flash memory systems. In a performance evaluation, BCMR prolonged the lifetime of NAND flash memory systems by 3.95%, on average and reduced a standard deviation per block by 7.4%, on average.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.5
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• pp.269-277
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• 2018

NAND flash memory has widely been used because of non-volatility, low power consumption and fast access time. However, it suffers from inability to provide update-in-place and the erase cycle is limited. The unit of read/write operation is a page and the unit of erase operation is a block. Moreover erase operation is slower than other operations. We proposed the Adaptive Garbage Collection (called "AGC") policy which focuses on not only reducing garbage collection process time for real-time guarantee but also wear-leveling for a flash memory lifetime. The AGC performs better than Cost-benefit policy and Greedy policy. But the AGC does not consider the operation characteristics. So we proposed the Advanced Adaptive Garbage Collection (called "A-AGC") policy which considers the page write operation count and block erase operation count. The A-AGC reduces the write operations by considering the data update frequency and update data size. Also, it reduces the erase operations by considering the file fragmentation. We implemented the A-AGC policy and measured the performance compared with the AGC policy. Simulation results show that the A-AGC policy performs better than AGC, specially for append operation.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.186-189
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• 2019

Image inpainting is an image processing technique that restores an image by naturally filling the empty or damaged regions in an image. In this paper, we present a new image inpainting technique that can suppress the generation of texture garbage which is one of the artifacts of existing exemplar-based image inpainting. Unlike the existing technique, only the stationary source patch is sampled as the exemplar patch based on the assumption of spatial stationarity of the texture. This prevents the texture garbage, which is an inconsistent piece of texture from being copied to the target region. Experimental results show that the texture synthesis using the proposed method produces more natural inpainting results than the existing method.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.27-35
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• 2021

NAND flash memory-based SSD needs an internal software, Flash Translation Layer(FTL) to provide traditional block device interface to the host because of its physical constraints, such as erase-before-write and large erase block. However, because useful host-side information cannot be delivered to FTL through the narrow block device interface, SSDs suffer from a variety of problems such as increasing garbage collection overhead, large tail-latency, and unpredictable I/O latency. Otherwise, the new type of SSD, open-channel SSD exposes the internal structure of SSD to the host so that underlying NAND flash memory can be managed directly by the host-level FTL. Especially, I/O data classification by using host-side information can achieve the reduction of garbage collection overhead. In this paper, we propose a new scheme to reduce garbage collection overhead of open-channel SSD by separating the journal from other file data for the journaling filesystem. Because journal has different lifespan with other file data, the Write Amplification Factor (WAF) caused by garbage collection can be reduced. The proposed scheme is implemented by modifying the host-level FTL of Linux and evaluated with both Fio and Filebench. According to the experiment results, the proposed scheme improves I/O performance by 46%~50% while reducing the WAF of open-channel SSDs by more than 33% compared to the previous one.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.109-120
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• 2024

Trash or garbage is one of the most dangerous health and environmental problems that affect pollution. Pollution affects nature, human life, and wildlife. In this paper, we propose modern solutions for cleaning the environment of trash pollution by enforcing strict action against people who dump trash inappropriately on streets, outside the home, and in unnecessary places. Artificial Intelligence (AI), especially Deep Learning (DL), has been used to automate and solve issues in the world. We availed this as an excellent opportunity to develop a system that identifies trash using a deep convolutional neural network (CNN). This paper proposes a real-time garbage identification system based on a deep CNN architecture with eight distinct classes for the training dataset. After identifying the garbage, the CCTV camera captures a video of the individual placing the trash in the incorrect location and sends an alert notice to the relevant authority.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.1-8
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• 2017

In this paper, we propose a new hybrid wear leveling technique for NAND Flash memory, called Time-Aware Wear Leveling (TAWL). Our proposal prolongs the lifetime of NAND Flash memory by using dynamic wear leveling technique which considers the wear level of hot blocks as well as static wear leveling technique which considers the wear level of the whole blocks. TAWL also reduces the overhead of garbage collection by separating hot data and cold data using update frequency rate. We showed that TAWL enhanced the lifetime of NAND flash memory up to 220% compared with previous wear leveling techniques and our technique also reduced the number of copy operations of garbage collections by separating hot and cold data up to 45%.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.672-674
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• 1998

Java virtual machine has the garbage collector that automate memory management. Mark-compact algorithm is one of the garbage collection algorithm that operating in 2 phases, marking and sweeping. One is Marking is marking live objects reachable from root object set. Sweeping is sweeping unmarked object from memory(return to free memory pool). This algorithm is easy to implement but cause a memory fragmentation. So compacting memory, before memory defragmentation become serious. When compacting memory, all other processes are suspended. It is critical for embedded system that must guarantee real-time processing. This paper introduce enhanced mark-compact garbage collection algorithm. Grouping the objects by their size that minimize memory fragmentation. Then apply smart algorithm to the grouped objects when allocating objects and compacting memory.