SAP SYSTEM DRILLING BLOCK, CLASSIFICATION AND CREATION OF APPROPRIATE PLATFORMS

For companies wishing to monitor inspection and repair processes through the system, the module proposed by SAP as part of the enterprise source planning application is the SAP PM (Plant Maintance) module. The PM module manages all equipment faults and scheduled repairs. The module, which is integrated with procurement, cost accounting, financial and inventory management processes, can automatically order for technical needs, track your current and overhaul costs immediately, and prepare spare parts. Purchase and consumption operations related to the warehouse can be carried out through tracking and system. These are the functions of the SAP PM module: Planning and implementation of repair activities; Repair for backup; Repair for the purpose of correction; Repair of spare parts in case of critical development of material in the warehouse; Capacity and resource planning; Maintenance planning in addition to repairs and etc. Key words: SAP system, management, planning, drilling, repair, control, organization

A Block Classification Method with Monitor and Restriction in NAND Flash memory

In this paper, we propose a block classification with monitor and restriction (BCMR) method to isolate and reduce the interference of blocks in garbage collection and wear leveling. The proposed method monitors the endurance variation of blocks during garbage collection and detects hot blocks by making a restriction condition based on this information. This method induces block classification by its update frequency for garbage collection and wear leveling, resulting in a prolonged lifespan for NAND flash memory systems. The performance evaluation results show that the BCMR method prolonged the life of NAND flash memory systems by 3.95% and reduced the standard deviation per block by 7.4%, on average.

Comparison of Coastline Extraction Methods and Block Classification Method to Extract Coastlines based on Remote Sensing

Coastlines change with urbanization, and methods to extract coastlines have been previously reported. However, comparisons of these methods are rare. Based on remote sensing image, methods of coastline extraction, namely, the visual interpretation method, the threshold segmentation method, improved normalized water indexes and edge detection algorithms and were studied in Xiamen City, China. The best method to extract coastlines was then determined. The results show that the visual interpretation method for coastline extraction was inefficient. The threshold segmentation method was suitable for small-scale, but not large-scale, coastline extraction, based on coastline area. Improved normalized water indexes were insensitive to sediment shadows. The Sobel method (edge detection algorithms) was suitable for large-scale coastline extraction but could yield false edges. Finally, the block classification method, which combines the advantages of different extraction methods, specifically the threshold segmentation method and improved normalized water indexes, was studied. The results of this study show that coastline extraction by the block classification method is easier and produces better results than coastline extraction by other methods. Therefore, block classification is recommended for the study of coastlines and coastal ecology in large areas.

Glossiness-aware Image Coding in JPEG Framework

In images, the representation of glossiness, translucency, and roughness of material objects (Shitsukan) is essential for realistic image reproduction. To date, image coding has been developed considering various indices of the quality of the encoded image, for example, the peak signal-to-noise ratio. Consequently, image coding methods that preserve subjective impressions of qualities such as Shitsukan have not been studied. In this study, the authors focus on the property of glossiness and propose a method of glossiness-aware image coding. Their purpose is to develop an encoding algorithm that produces images that can be decoded by standard JPEG decoders, which are commonly used worldwide. The proposed method consists of three procedures: block classification, glossiness enhancement, and non-glossiness information reduction. In block classification, the types of glossiness in a target image are classified using block units. In glossiness enhancement, the glossiness in each type of block is emphasized to reduce the amount of degradation of glossiness during JPEG encoding. The third procedure, non-glossiness information reduction, further compresses the information while maintaining the glossiness by reducing the information in each block that does not represent the glossiness in the image. To test the effectiveness of the proposed method, the authors conducted a subjective evaluation experiment using paired comparison of images coded by the proposed method and JPEG images with the same data size. The glossiness was found to be better preserved in images coded by the proposed method than in the JPEG images.