Improving Application Launch Performance on Solid State Drives

Application launch performance is of great importance to system platform developers and vendors as it greatly affects the degree of users’ satisfaction.The single most effective way to improve application launch performance is to replace a hard disk drive (HDD) with a solid state drive (SSD),which has recently become affordable and popular.A natural question is then whether or not to replace the traditional HDD-aware application launchers with a new SSD-aware optimizer.We address this question by analyzing the inefficiency of the HDD-aware application launchers on SSDs and then proposing a new SSD-aware application prefetching scheme,called the Fast Application STarter (FAST).The key idea of FAST is to overlap the computation (CPU) time with the SSD access (I/O) time during an application launch.FAST is composed of a set of user-level components and system debugging tools provided by Linux OS (operating system).Hence,FAST can be easily deployed in any recent Linux versions without kernel recompilation.We implement FAST on a desktop PC with an SSD running Linux 2.6.32 OS and evaluate it by launching a set of widely-used applications,demonstrating an average of 28% reduction of application launch time as compared to PC without a prefetcher.     

Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting

This study considers real estate appraisal forecasting problem. While there is a great deal of literature about use of artificial intelligence and multiple linear regression for the problem, there has been always controversy about which one performs better. Noting that this controversy is due to difficulty finding proper predictor variables in real estate appraisal, we propose a modified version of ridge regression, i.e., ridge regression coupled with genetic algorithm (GA-Ridge). In order to examine the performance of the proposed method, experimental study is done for Korean real estate market, which verifies that GA-Ridge is effective in forecasting real estate appraisal. This study addresses two critical issues regarding the use of ridge regression, i.e., when to use it and how to improve it.     

Removal efficiencies of endocrine disrupting chemicals by coagulation/flocculation, ozonation, powdered/granular activated carbon adsorption, and chlorination

Removal efficiencies of endocrine disrupting chemicals (EDCs), bisphenol A and nonylphenol, during various types of water treatment processes were evaluated extensively using laboratory- and pilot-scale experiments. The specific processes of interest were coagulation/flocculation sedimentation/filtration (conventional water treatment process), powdered activated carbon (PAC), granular activated carbon (GAC), ozonation and chlorination. Batch sorption tests, coagulation tests, and ozone oxidation tests were also performed at higher concentrations with 14 EDCs including bisphenol A. The conventional water treatment process had very low removal efficiencies (0 to 7%) for all the EDCs except DEHP, DBP and DEP that were removed by 53%, 49%, and 46%, respectively. Ozonation at 1 mgO₃/ L removed 60% of bisphenol A and 89% of nonylphenol, while chlorination at 1 mg/L removed 58% and 5%, respectively. When ozone and chlorine doses were 4 and 5 mg/L, respectively, both EDCs were not detected. PAC removal efficiencies ranged from 15% to 40% at 3 to 10 mg/L of PAC with a contact time of 15 minutes. In the high concentration batch sorption tests, EDC removal efficiencies by PAC were closely related to octanol-water partition coefficient (K_ow). GAC adsorption was very effective water treatment process. The type and service time of GAC did not affect EDC removal efficiencies. The combination of ozonation and GAC in series appears to remove EDCs effectively to safe levels while conventional water treatment could not.     

Preparation of Pt supported on mesoporous carbons for the reduction of oxygen in polymer electrolyte membrane fuel cell (PEMFC)

Porous carbon materials (SM-C, HS-C and TM-C) were prepared using commercial colloidal silicas (SM-30, HS-40 and TM-50) and a resorcinol-formaldehyde resin as a removable template and a carbon precursor, respectively. All of the prepared carbons had high surface areas with narrow pore size distributions. In particular, the pore diameter of the carbons could be controlled over a range of mesopore size by the use of an appropriate silica employed as a template. Mesoporous carbon templated using TM-50 had the largest pore size, while that for SM-C, was the smallest. Pt nanoparticles were supported on these mesoporous carbons for use as a catalyst in a polymer electrolyte membrane fuel cell (PEMFC). The crystallite size of the Pt catalyst was found to be closely related to the properties of the corresponding carbon support. A carbon support with a large pore size and a high surface roughness was found to favor the dispersion of Pt crystallite. In a single cell test, the Pt catalysts supported on mesoporous carbons exhibited higher cell performance than that on activated carbon. In particular, the Pt/TM-C catalyst showed the best cell performance among the catalysts tested. In addition to the high surface area of the active metal, the large pore size of the Pt/TM-C appears to have positive effect on the distribution of ionomer, resulting in facile formation of a triple-phase boundary.     

The preparation and characterization of porous carbons for hydrogen storage

Porous carbon materials with a cylindrical pore structure were prepared using ordered mesoporous silica as a removable template. To investigate the effect of the structural and textural properties of the products on hydrogen adsorption capacity, different carbon precursors and synthetic methods were used in their preparation. All of the carbon materials prepared showed a well-defined pore structure with a high surface area irrespective of the carbon precursor used in the preparation. Hydrogen adsorption tests indicated that the capacity of the materials for hydrogen adsorption was highly dependent on total surface area and the pore structure. Based on the N₂ sorption results, the total surface area was directly correlated with the hydrogen adsorption capacity.     

A Study on the Pd/a-Si/Ni Seed Layer for Metal-Induced Lateral Crystallization and Poly-Si TFTs

The effect of Pd on the growth rate of metal-induced lateral crystallization (MILC) from Ni seed and the electrical properties of thin-film transistors (TFTs) fabricated on the films crystallized by MILC were investigated. When the Pd metal is placed on the amorphous-silicon/Ni-seed layer, the MILC growth rate is two to three times faster than that of conventional Ni-MILC, without any degradation of TFTs. These results were explained by a stress that is generated by the formation of Pd₂Si     

A fast path planning by path graph optimization

A fast path planning method by optimization of a path graph for both efficiency and accuracy is proposed. A conventional quadtree-based path planning approach is simple, robust, and efficient. However, it has two limitations. We propose a path graph optimization technique employing a compact mesh representation. A world space is triangulated into a base mesh and the base mesh is simplified to a compact mesh. The compact mesh representation is object-dependent; the positions of vertexes of the mesh are optimized according to the curvatures of the obstacles. The compact mesh represents the obstacles as accurately as the quadtree even though using much fewer vertexes than the quadtree. The compact mesh distributes vertexes in a free space in a balanced way by ensuring that the lengths of edges are below an edge length threshold. An optimized path graph is extracted from the compact mesh. An iterative vertex pushing method is proposed to include important obstacle boundary edges in the path graph. Dijkstra's shortest path searching algorithm is used to search the shortest path in the path graph. Experimental results show that the path planning using the optimized path graph is an order of magnitude faster than the quadtree approach while the length of the path generated by the proposed method is almost the same as that of the path generated by the quadtree.     

An agent for selecting optimal order set in EC marketplace

For most small manufacturing companies, the selection and completion of orders placed by buyers are closely linked to the load status of their production lines. The decision to accept an order, or the selection of optimal order set, critically depends on the production schedule when orders exceed production capacity. However, production scheduling is mainly performed by human experts, and small companies lack resources to implement such a task. As a result, most small manufacturers suffer from the difficulty to meet due dates or to make proper decision in accepting new orders. To address this problem, this paper develops an automatic agent that selects an optimal set of orders using commonly available Internet technology. The main engine of the selection agent is based on a typical job shop scheduling model, formulating and implementing it as an Integer Program (IP) model. We also translate IP into Genetic Algorithm (GA) to address its NP-hard problem. We conclude with a suggestion for an agent architecture that tackles Web-based order selection problems.     

Data‐Driven Kinematic Control for Robotic Spatial Augmented Reality System with Loose Kinematic Specifications

We propose a data‐driven kinematic control method for a robotic spatial augmented reality (RSAR) system. We assume a scenario where a robotic device and a projector‐camera unit (PCU) are assembled in an ad hoc manner with loose kinematic specifications, which hinders the application of a conventional kinematic control method based on the exact link and joint specifications. In the proposed method, the kinematic relation between a PCU and joints is represented as a set of B‐spline surfaces based on sample data rather than analytic or differential equations. The sampling process, which automatically records the values of joint angles and the corresponding external parameters of a PCU, is performed as an off‐line process when an RSAR system is installed. In an on‐line process, an external parameter of a PCU at a certain joint configuration, which is directly readable from motors, can be computed by evaluating the pre‐built B‐spline surfaces. We provide details of the proposed method and validate the model through a comparison with an analytic RSAR model with synthetic noises to simulate assembly errors.