Proactive thermal management in green datacenters

The increasing demand for faster computing and high storage capacity has resulted in an increase in energy consumption and heat generation in datacenters. Because of the increase in heat generation, cooling requirements have become a critical concern, both in terms of growing operating costs as well as their environmental and societal impacts. Presently, thermal management techniques make an effort to thermally profile and control datacenters’ cooling equipment to increase their efficiency. In conventional thermal management techniques, cooling systems are triggered by the temperature crossing predefined thresholds. Such reactive approaches result in delayed response as the temperature may already be too high, which can result in performance degradation of hardware.     

PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop. The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV. The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline. We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision. We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP problem.     

Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation

This study presents an efficient variational region-based active contour model for segmenting images without priori knowledge about the object or background. In order to handle intensity inhomogeneities and noise, we propose to integrate into the region-based local intensity model a global density distance inspired by the Bhattacharyya flow. The local term based on local information of segmented image allows the model to deal with bias field artifact, which arises in data acquisition processes. The global term, which is based on the density distance between the probability distribution functions of image intensity inside and outside the active contour, provides information for accurate segmentation, keeps the curve from spilling, and addresses noise in the image. Intensive 2D and 3D experiments on many imaging modalities of medical fields such as computed tomography, magnetic resonance imaging, and ultrasound images demonstrate the effectiveness of the model when dealing with images with blurred object boundary, intensity inhomogeneities, and noise.     

Tracking servo method for holographic data storage using discrete pre-patterns

Page-oriented holographic data storage (HDS) is very sensitive to disturbances that affect the position of the recording medium. Accordingly, a more precise tracking servo is required for the recording process, and is also crucial for achieving high storage density. A compensation method is therefore essential for HDS recording. In this paper, we suggest some discrete pre-patterns for the tracking servo used in the recording process. This method is motivated by a tracking servo technique for a hard disk drive. Firstly, in designing the pattern shape, HDS characteristics are taken into account. Secondly, track error signals are analyzed. Thirdly, the discrete pre-pattern intervals are determined according to the track tolerance. Lastly, the feasibility of the new method is analyzed via simulations and experiments.     

Development of a decision making system for selection of dental implant abutments based on the fuzzy cognitive map

Dental implant is a medical operation used to restore the functions of damaged or missing teeth. Correct implantation requires the proper selection of size and shape among the implant structures. In this paper, we propose a method of constructing a Web-based decision making system that enables the selection of a suitable type of abutment by taking into account the patient’s anatomical data and preferences that are based on an expert’s knowledge and experience for those patients. After the classification of the types of abutment that can be connected to fixtures of implants, we built a knowledge base and case base library according to the characteristics of osseous tissue and teeth shape to select optimal abutment. Moreover, we introduce a fuzzy cognitive map that takes into consideration expert’s knowledge for factors that affect implantation. After the determination of the cause-and-effect relationship among the concepts of the fuzzy cognitive map, an osseointegration factor with the highest conceptual concentration weight is inferred from the decision making system. In addition, the selection process for abutment is expressed as a decision making tree and then, it is applied for the rule-based reasoning and case-based reasoning. The optimized selection result is finally extracted based on the fuzzy membership function using fuzzy inference.     

Effects of user age and target-expansion methods on target-acquisition tasks using a mouse

Target expansion, i.e., the increase of target size according to cursor movement, can be a practical scheme to improve the usability of target-selection tasks using a mouse. This study examined the effects of different user age groups and target-expansion methods on target-acquisition tasks with grouped icons. Twenty-eight subjects performed acquisition tasks under eight experimental conditions: combinations of four expansion areas (no, one-icon, fish-eye, and group expansion) and two expansion techniques (occlusion and push). Older users took longer to acquire targets than younger users; however, they showed no significant difference in accuracy. Target expansion did not substantially improve performance speed compared to the static condition. However, the error rate was lowest when group area was expanded with the push technique, and both age groups were most satisfied with one-icon area expansion with the occlusion technique. We suggest alternative guidelines in designing target-expansion schemes.     

Integration of evolutional BOMs for design of ship outfitting equipment

PLM (Product Lifecycle Management) as well as enterprise information systems commonly use the BOM (Bill of Material) as a means of communication during product development. BOM has been used for product design, production scheduling, procurement, manufacturing and maintenance as it contains the part list of a subassembly or assembly product. BOM currently plays a key role in the PLM environment because it is an essential product information platform in the industry. In practice, product data should be arranged based on the BOM and product structure. Many shipyards or PLM vendors have attempted to develop a PLM adequate for shipbuilding. However, it is always the premise of the successful PLM implementation to prepare the efficient product structure and BOM.This paper suggests an enterprise BOM which addresses the hierarchy of parts and assembly, product structure and product information of outfitting equipment in marine vessel design. In order to express the evolution of product structures and outfitting information during the ship design, the enterprise BOM is modeled by the structure BOM and the display BOM. Concepts are developed for modeling all levels of product relations with a uniform set of relationships supported by the enterprise BOM. It is shown that the architecture of the structure BOM can be used to define the relationships between assemblies, parts, and a multi-view of an evolutional BOM in ship design. The architecture of the outfitting BOM is also presented using an example of a piping system.     

Constraint force design method for topology optimization of planar rigid-body mechanisms

This study develops a new design method called the constraint force design method, which allows topology optimization for planar rigid-body mechanisms. In conventional mechanism synthesis methods, the kinematics of a mechanism are analytically derived and the positions and types of joints of a fixed configuration (hereafter the topology) are optimized to obtain an optimal rigid-body mechanism tracking the intended output trajectory. Therefore, in conventional methods, modification of the configuration or topology of joints and links is normally considered impossible. In order to circumvent the fixed topology limitation in optimally designing rigid-body mechanisms, we present the constraint force design method. This method distributes unit masses simulating revolute or prismatic joints depending on the number of assigned degrees of freedom, analyzes the kinetics of unit masses coupled with constraint forces, and designs the existence of these constraint forces to minimize the root-mean-square error of the output paths of synthesized linkages and a target linkage using a genetic algorithm. The applicability and limitations of the newly developed method are discussed in the context of its application to several rigid-body synthesis problems.     

Makespan minimization for scheduling unrelated parallel machines with setup times

This study considers the problem of scheduling jobs on unrelated parallel machines with machine-dependent and job sequence-dependent setup times. In this study, a restricted simulated annealing (RSA) algorithm which incorporates a restricted search strategy is presented to minimize the makespan. The proposed RSA algorithm can effective reduce the search effort required to find the best neighborhood solution by eliminating ineffective job moves. The effectiveness and efficiency of the proposed RSA algorithm is compared with the basic simulated annealing and existing meta-heuristics on a benchmark problem dataset used in earlier studies. Computational results indicate that the proposed RSA algorithm compares well with the state-of-the-art meta-heuristic for small-sized problems, and significantly outperforms basic simulated annealing algorithm and existing algorithms for large-sized problems.