Forecasting trends of high-frequency KOSPI200 index data using learning classifiers

Recently many statistical learning techniques have been applied to the prediction of financial variables. The aim of this paper is to conduct a comprehensive study of the applications of statistical learning techniques to predict the trend of the return of high-frequency Korea composite stock price index (KOSPI) 200 index data using the information from the one-minute time series of spot index, futures index, and foreign exchange rate. Through experiments, it is observed that the spot index change is better predictable with high-frequency time series data and the futures index information significantly improves the prediction accuracy of the return trends of the spot index for high-frequency index data, while the information of exchange rate does not. Also, dimension reduction process before training helps to increase the accuracy and dramatically for some classifiers. In addition, the trained classifiers with which a virtual trading strategy is applied to, noticeable better profits can be achieved than just a buy-and-hold-like strategy.     

Grain dependence of ferroelectric domain switching on SrBi2Ta2O9 thin films observed by Kelvin probe force microscope

We have studied grain-shape dependence of Kelvin probe force microscopy of SrBi₂Ta₂O₉ thin films on epitaxial La_0.5Sr_0.5CoO₃/LaAlO₃ substrates. By changing the growth condition in pulsed laser deposition, we have grown the SrBi₂Ta₂O₉ thin films with various grain shapes. The shape and the orientation of SrBi₂Ta₂O₉ the thin films with various growth conditions have been analyzed by X-ray diffraction and scanning electron microscope. The large number of the long rectangular grains was observed accompanied with relatively larger (220) peaks than other peaks. From the Kelvin probe force microscope study, it has been observed that the long rectangular grains showed characteristics of easy ferroelectric domain switching at a low writing bias and weaker influence of surface charges.     

Generalization of active filters for EMI reduction and harmonics compensation

This paper provides generalized analysis of active filters used as electromagnetic interference (EMI) filters and active-power filters. Insertion loss and impedance increase of various types of active-filter topologies are described with applicable requirements and limitations as well as the rationale for selecting active-filter topology according to different applications.     

Pyroelectric properties and X-ray photoelectron spectroscopic study of R.F. magnetron-sputtering-derived PZT thin films deposited on various interlayers

PZT thin films and interlayers were fabricated by the radio frequency (r.f.) Magnetron-sputtering from the Pb_1.1Zr_0.53Ti_0.47O₃, PbO and TiO₂ target. As a result of the XPS depth profile analysis, we can confirm that the substrate temperature affects the oxidation condition of each element of interlayers and the PZT film. Compared to the PZT/Pt structure, the dielectric and pyroelectric properties of PZT thin films inserted by interlayers were measured to a relatively high value. In particular, the PZT/PbO structure had the highest pyroelectric properties (P = 189.4 μC/cm²K; F _D = 12.7×10⁻⁶ Pa^−1/2; F _V = 0.018 m²/C).     

On Using Real-Time Static Locking Protocols for Distributed Real-Time Databases

The use of Static Two Phase Locking Protocols (S2PL) for concurrency control in real-time database systems (RTDBS) has received little attention in the past. Actually, real-time S2PL (RT-S2PL) protocols do possess some desirable features making them suitable for RTDBS, especially for distributed real-time database systems (DRTDBS) in which remote locking is required and distributed deadlock is possible. In this paper, different RT-S2PL protocols are proposed. They differ in their methods of reducing the blocking time of higher priority transactions. Their performance is studied and compared with a real-time dynamic two phase locking protocol (RT-D2PL), called Hybrid Two Phase Locking (Hb2PL). The impact of different system and workload parameters, such as mean inter-arrival time of transactions, number of remote lock requests of a transaction, communication overhead for sending messages, and database size on their performance have been examined. The performance results indicate that the RT-S2PL protocols are suitable for DRTDBS in which the proportion of local locks of a transaction is small and the communication overhead for locking is high.     

Managing deadline miss ratio and sensor data freshness in real-time databases

The demand for real-time data services is increasing in many applications including e-commerce, agile manufacturing, and telecommunications network management. In these applications, it is desirable to execute transactions within their deadlines, i.e., before the real-world status changes, using fresh (temporally consistent) data. However, meeting these fundamental requirements is challenging due to dynamic workloads and data access patterns in these applications. Further, transaction timeliness and data freshness requirements may conflict. We define average/transient deadline miss ratio and new data freshness metrics to let a database administrator specify the desired quality of real-time data services for a specific application. We also present a novel QoS management architecture for real-time databases to support the desired QoS even in the presence of unpredictable workloads and access patterns. To prevent overload and support the desired QoS, the presented architecture applies feedback control, admission control, and flexible freshness management schemes. A simulation study shows that our QoS-aware approach can achieve a near zero miss ratio and perfect freshness, meeting basic requirements for real-time transaction processing. In contrast, baseline approaches fail to support the desired miss ratio and/or freshness in the presence of unpredictable workloads and data access patterns.     

A systematic approach for model refinement considering blind and recognized uncertainties in engineered product development

In recent years, virtual testing has played an increasingly important role in the design and evaluation of engineered products. However, it is challenging to build the highly accurate computational models for virtual testing. Blind and recognized uncertainties are often unintentionally incorporated. These uncertainties consequently decrease the predictive capability of the models. To this end, this paper proposes a systematic approach for model refinement that minimizes the impact of unrecognized blind and recognized epistemic uncertainties in computational modeling. The approach consists of three steps: model invalidity analysis (MIA), development of an invalidity reasoning tree (IRT), and invalidity sensitivity analysis (ISA). First, in the MIA, possible causes that lead to discrepancies between the experimental and simulation responses are identified through brainstorming. Next, the IRT is built using the affinity diagram. It sequentially lists and screens potential candidate issues for model refinement at the stages of conceptual, mathematical, and computational modeling. Finally, the ISA quantifies the effect of incorporating updates in the model to address potential candidate issues with the goal of reducing the impact of the blind and recognized uncertainties. The most critical candidates are determined by using a weighted decision matrix. To demonstrate the effectiveness of the proposed approach, a case study examining a smartphone liquid crystal display fracture is presented.     

Moment-Based Angular Difference Estimation Between Two Tomographic Projections in 2D and 3D

This paper introduces a new method for estimating the angular difference between two tomographic projections belonging to a set of projections taken at unknown directions in 2D and 3D. Our method relies on the projection neighbor selection in projection moment space, the calculation of the angular differences between these neighboring projections using moment properties and a projection moment neighborhood graph. The accuracy and the robustness of our method are shown on a test database including fifty 2D and 3D gray-level images at different resolutions and with different levels of noise.     

Advancements in Dry Powder Delivery to the Lung

The dry powder inhaler (DPI) has become widely known as a very attractive platform for drug delivery. Many patients have traditionally used DPIs to treat asthma and chronic obstructive pulmonary disease. Recently, the development of new DPIs for delivering therapeutic proteins such as insulin has been accelerated by patient demands, and innovative research. The current market for DPIs has over 20 devices presently in use, and many devices under development for delivering a variety of therapeutic agents. DPIs are recognized as suitable alternatives to pressurized metered dose inhalers for some patients, but the performance of DPI devices may vary according to a given patient's physiological condition. This variation can be associated with the necessary powder dispersion mechanism of each device. As such, much interest has focused on the development of efficient powder dispersion mechanisms, as this effectively minimizes the influence of interpatient variability. This article reviews DPI devices currently available, advantages of newly developed devices, outlines some requirements for future device design.