Vector control for loss minimization of induction motor using GA–PSO

This paper deals with the GA–PSO (genetic algorithm–particle swarm optimization) based vector control for loss minimization operation of induction motor. It is estimated that more than around 50% of the world electric energy generated is consumed by electric machines such as induction motor, dc motor. So, improving efficiency in electric drives is important and control strategy for minimum energy loss is needed as one of optimal operation strategies. The vector control of induction motor has been widely used to operate in a wide speed range by using flux weakening at rated speed. However, it is still necessary to advance because of coupling is behavior between fluxes in motor. In this paper, vector control approach is suggested for an optimal operation of induction motor using variable acceleration and GA–PSO tuning method through simulation. We can obtain satisfactory results for energy saving control.     

Proteomics in diabetic nephropathy

Diabetes mellitus (DM) is currently one of the principal causes of end stage renal disease (ESRD). Approximately 40% of all diabetic patients eventually develop diabetic nephropathy (DN). The complexity of diabetes and its complications require a broad-based, unbiased, scientific approach, such as proteomics, in order to understand the progression of DN. Proteomic techniques have been applied extensively to explore the complexity of the mechanisms associated with DN, and to identify novel biomarkers and therapeutic targets. This review provides insights into how proteomics can be applied to DN, and how experimental data can be linked to clinical applications. In addition, recent proteome studies of DN are summarized. The rapid rate of development of the relevant technologies, along with the combination of classic physiological and biochemical techniques with proteomics will facilitate new discoveries.     

Evaluation of the critical stroke of an earthworm-like robot for capsule endoscopes

Recently, the capsule endoscope has been highlighted for the patient's convenience and the possibility of application in the small intestine. However, the capsule endoscope has some limitations in obtaining an image of the digestive organ because its movement depends only on the peristaltic motion. In order to solve these problems, it is necessary to determine the locomotive mechanism of the capsule endoscope. Therefore, the present authors have already proposed an earthworm-like robot, which has a locomotive mechanism. However, this mechanism should be designed so that the earthworm-like robot has a larger stroke than the critical stroke required to perform motion inside the small intestine. In this study, therefore, not only is the modelling of the locomotive process based on a biomechanical study presented but also the movement of the earthworm-like robot in the small intestine is simulated. Through the simulation process, the variation in the critical stroke with regard to the elastic modulus of the mesentery is investigated. Finally, from an in vitro test of the proposed robot, it is found that the experimental result is very similar to that of the simulation. Consequently, the present work will provide guidelines for designing an earthworm-like robot for diagnosis of the small intestine.     

Arthemis: annotation software in an integrated capturing and analysis system for colonoscopy

Colonoscopy is an endoscopic technique that allows physicians to inspect the inside of the human colon. During a colonoscopic procedure, a tiny video camera at the tip of the endoscope generates a video signal of the internal mucosa of the colon. In current practice, the entire colonoscopic procedure is not routinely captured. Software tools providing easy access to important contents of videos that are digitally captured during colonoscopy are not available. Hence, it is very time consuming to review an entire video, locate important contents, annotate them, and extract the annotated contents for research, teaching, and training purposes. Arthemis, a software application, was developed to facilitate this process. For convenient data sharing, Arthemis allows annotation according to the European Gastrointestinal Society for Endoscopy (ESGE) Minimal Standard Terminology (MST), an internationally accepted standard for digestive endoscopy. Arthemis is part of our integrated capturing and content analysis system for colonoscopy called Endoscopic Multimedia Information System (EMIS). This paper presents Arthemis as a component of EMIS, the design and implementation of Arthemis, and key lessons learned from the development process.     

Multi-time-scale heat transfer modeling of turbid tissues exposed to short-pulsed irradiations

A combined hyperbolic radiation and conduction heat transfer model is developed to simulate multi-time-scale heat transfer in turbid tissues exposed to short-pulsed irradiations. An initial temperature response of a tissue to an ultrashort pulse irradiation is analyzed by the volume-average method in combination with the transient discrete ordinates method for modeling the ultrafast radiation heat transfer. This response is found to reach pseudo steady state within 1 ns for the considered tissues. The single pulse result is then utilized to obtain the temperature response to pulse train irradiation at the microsecond/millisecond time scales. After that, the temperature field is predicted by the hyperbolic heat conduction model which is solved by the MacCormack's scheme with error terms correction. Finally, the hyperbolic conduction is compared with the traditional parabolic heat diffusion model. It is found that the maximum local temperatures are larger in the hyperbolic prediction than the parabolic prediction. In the modeled dermis tissue, a 7% non-dimensional temperature increase is found. After about 10 thermal relaxation times, thermal waves fade away and the predictions between the hyperbolic and parabolic models are consistent.     

Data cache and direct memory access in programming mediaprocessors

Mediaprocessors provide high performance by using both instruction- and data-level parallelism. Because of the increased computing power, transferring data between off- and on-chip memories without slowing down the core processor's performance is challenging. Two methods, data cache and direct memory access, address this problem in different ways     

Hierarchical ATPG for analog circuits and systems

Automatic test-pattern generation (ATPG) algorithms for analog circuits have been under intense investigation for the last several years. As system design aggressively moves to system-on-a-chip (SoC) and core-based integration, hierarchical analog ATPG emerges as an even more difficult challenge. Attempts to develop an effective algorithm have had varying degrees of success. This article reviews some fundamental issues and recent work in hierarchical analog ATPG and presents an algorithm based on controllability and observability computation. This algorithm has been implemented in a prototype tool, and results based on several case studies show the application of the technique     

Collocation Dictionary Optimization Using WordNet and k-Nearest Neighbor Learning

In machine translation, collocation dictionaries are important for selecting accurate target words. However, if the dictionary size is too large it can decrease the efficiency of translation. This paper presents a method to develop a compact collocation dictionary for transitive verb–object pairs in English–Korean machine translation without losing translation accuracy. We use WordNet to calculate the semantic distance between words, and k-nearestneighbor learning to select the translations. The entries in the dictionary are minimized to balance the trade-off between translation accuracy and time. We have performed several experiments on a selected set of verbs extracted from a raw corpus of over 3 million words. The results show that in real-time translation environments the size of a collocation dictionary can be reduced up to 40% of its original size without significant decrease in its accuracy.     

A grid-based node split algorithm for managing current location data of moving objects

There is rapidly increasing interest in Location Based Service (LBS) which utilizes location data of moving objects. To efficiently manage the huge amounts of location data in LBS, the GALIS (Gracefully Aging Location Information System) architecture, a cluster-based distributed computing architecture, is proposed. The GALIS using the non-uniform 2-level grid algorithm performs load balancing and indexing for nodes. However, the non-uniform 2-level grid algorithm has a problem creating unnecessary nodes when moving objects are crowded in a certain region. Therefore, a new node split algorithm, which is more efficient for various distribution of moving objects, is proposed in this paper. Because the algorithm proposed in this paper considers spatial distribution for the current location of moving objects, it can perform efficient load balancing without creating unnecessary nodes even when moving objects are congested in a certain region. Besides, the various data distribution configuration for moving objects has been experimented by implementing node split simulators and it’s been verified that the proposed algorithm can split nodes more efficiently than the existing algorithm.