• Journal of Computational Design and Engineering
• /
• v.2 no.4
• /
• pp.261-267
• /
• 2015

Free-form blades are widely used in different industries, such as aero-engine and steam turbine. Blades that are damaged during service or have production deficiencies are usually replaced with new ones. This leads to the waste of expensive material and is not sustainable. However, material and costs can be saved by repairing of locally damaged blades or blades with localized production deficiencies. The blade needs to be further machined after welding process to reach the aerodynamic performance requirements. This paper outlines an adaptive location approach of repaired blade for model reconstruction and NC machining. Firstly, a mathematical model is established to describe the localization problem under constraints. Secondly, by solving the mathematical model, localization of repaired blade for NC machining can be obtained. Furthermore, a more flexible method based on the proposed mathematical model and the continuity of the deformation process is developed to realize a better localization. Thirdly, by rebuilding the model of the repaired blade and extracting repair error, optimized tool paths for NC machining is generated adaptively for each individual part. Finally, three examples are given to validate the proposed method.

• ETRI Journal
• /
• v.34 no.5
• /
• pp.666-673
• /
• 2012

Non-real-time delivery of stereoscopic video has been considered as a service scenario for 3DTV to overcome the limited bandwidth in the terrestrial digital television system. A hybrid codec combining MPEG-2 and H.264/AVC has been suggested for the compression of stereoscopic video for 3DTV. In this paper, we propose a stereoscopic video coding scheme using adaptive pre-/post-filters (APPF) to improve the quality of 3D video while retaining compatibility with legacy video coding standards. The APPF are applied adaptively to blocks of various sizes determined by the macroblock coding mode and reference frame index. Experiment results show that the proposed method achieves up to 24.86% bit rate savings relative to a hybrid codec of MPEG-2 and H.264/AVC including the inter-view prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.6
• /
• pp.2144-2159
• /
• 2015

CISCO VNI predicted an average annual growth rate of 66% for mobile video traffic between 2014 and 2019 and accordingly much academic research related to video streaming has been initiated. In video streaming, Adaptive Bitrate (ABR) is a streaming technique in which a source video is stored on a server at variable encoding rates and each streaming user requests the most appropriate video encoding rate considering their channel capacity. However, these days, ABR related studies are only focusing on real-time rate adaptation omitting energy efficiency though it is one of the most important requirement for mobile devices, which may cause dissatisfaction for streaming users. In this paper, we propose an energy efficient prefetching based dynamic adaptive streaming technique by considering the limited characteristics of the batteries used in mobile devices, in order to reduce the energy waste and provide a similar level of service in terms of the average video rate compared to the latest ABR streaming technique which does not consider the energy consumption. The simulation results is showing that our proposed scheme saves 65~68% of energy at the average global mobile download speed compared to the latest high performance ABR algorithm while providing similar rate adaptation performance.

• Journal of Korea Multimedia Society
• /
• v.20 no.9
• /
• pp.1559-1566
• /
• 2017

Recently in the IoT(Internet of Things) environment, a data collection in real-time through device's sensor has increased with an emergence of various devices. Collected data from IoT environment shows a large scale, non-uniform generation cycle and atypical. For this reason, the distributed processing technique is required to analyze the IoT sensor data. However if you do not consider the optimal scheduling for data and the processor of IoT in a distributed processing environment complexity increase the amount in assigning a task, the user is difficult to guarantee the QoS(Quality of Service) for the sensor data. In this paper, we propose APQTA(Adaptive Priority Queue-driven Task Allocation method for sensor data processing) to efficiently process the sensor data generated by the IoT environment. APQTA is to separate the data into job and by applying the priority allocation scheduling based on the deadline to ensure that guarantee the QoS at the same time increasing the efficiency of the data processing.

• KIISE Transactions on Computing Practices
• /
• v.21 no.2
• /
• pp.160-165
• /
• 2015

Traditional speech emotion recognition techniques recognize emotions using a general training model based on the voices of various people. These techniques can not consider personalized speech character exactly. Therefore, the recognized results are very different to each person. This paper proposes an adaptive speech emotion recognition framework made from user's' immediate feedback data using a prompted labeling technique for building a personal adaptive recognition model and applying it to each user in a mobile device environment. The proposed framework can recognize emotions from the building of a personalized recognition model. The proposed framework was evaluated to be better than the traditional research techniques from three comparative experiment. The proposed framework can be applied to healthcare, emotion monitoring and personalized service.

• Journal of Communications and Networks
• /
• v.16 no.5
• /
• pp.512-522
• /
• 2014

This paper proposes fast millimeter-wave (mm-wave) beam training protocols with receive beamforming. Both IEEE standards and the academic literature have generally considered beam training protocols involving exhaustive search over all possible beam directions for both the beamforming initiator and responder. However, this operation requires a long time (and thus overhead) when the beamwidth is quite narrow such as for mm-wave beams ($1^{\circ}$ in the worst case). To alleviate this problem, we propose two types of adaptive beam training protocols for fixed and adaptive modulation, respectively, which take into account the unique propagation characteristics of millimeter waves. For fixed modulation, the proposed protocol allows for interactive beam training, stopping the search when a local maximum of the power angular spectrum is found that is sufficient to support the chosen modulation/coding scheme. We furthermore suggest approaches to prioritize certain directions determined from the propagation geometry, long-term statistics, etc. For adaptive modulation, the proposed protocol uses iterative multi-level beam training concepts for fast link configuration that provide an exhaustive search with significantly lower complexity. Our simulation results verify that the proposed protocol performs better than traditional exhaustive search in terms of the link configuration speed for mobile wireless service applications.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.4
• /
• pp.1424-1441
• /
• 2014

Two-tier femtocell networks, consisting of a conventional cellular network underlaid with femtocell hotspots, play an important role in the indoor coverage and capacity of cellular networks. However, the cross- and co-tier interference will cause an unacceptable quality of service (QoS) for users with universal frequency reuse. In this paper, we propose a novel downlink interference mitigation strategy for spectrum-shared two-tier femtocell networks. The proposed solution is composed of three parts. The first is femtocells clustering, which maximizes the distance between femtocells using the same slot resource to mitigate co-tier interference. The second is to assign macrocell users (MUEs) to clusters by max-min criterion, by which each MUE can avoid using the same resource as the nearest femtocell. The third is a novel adaptive power control scheme with femtocells downlink transmit power adjusted adaptively based on the signal to interference plus noise ratio (SINR) level of neighboring users. Simulation results show that the proposed scheme can effectively increase the successful transmission ratio and ergodic capacity of femtocells, while guaranteeing QoS of the macrocell.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.9
• /
• pp.4242-4263
• /
• 2017

Multi-User Multiple-Input Multiple-Output (MU-MIMO) technology has recently attracted significant attention from academia and industry because of it is increasingly important role in improving networks' capacity and data rate. Moreover, MU-MIMO systems for the Fifth Generation (5G) have already been researched. High Quality of Service (QoS) and efficient operations at the Medium Access Control (MAC) layer have become key requirements. In this paper, we propose a downlink MU-MIMO MAC protocol based on adaptive Channel State Information (CSI) feedback (called MMM-A) for Wireless Local Area Networks (WLANs). A modified CSMA/CA mechanism using new frame formats is adopted in the proposed protocol. Specifically, the CSI is exchanged between stations (STAs) in an adaptive way, and a packet selection strategy which can guarantee a fairer QoS for scenarios with differentiated traffic is also included in the MMM-A protocol. We then derive the expressions of the throughput and access delay, and analyze the performance of the protocol. It is easy to find that the MMM-A protocol outperforms the commonly used protocols in terms of the saturated throughput and access delay through simulation and analysis results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.7
• /
• pp.3057-3075
• /
• 2020

End-to-end network delay plays an vital role in distributed services. This delay is used to measure QoS (Quality-of-Service). It would be beneficial to know all node-pair delay information, but unfortunately it is not feasible in practice because the use of active probing will cause a quadratic growth in overhead. Alternatively, using the measured network delay to estimate the unknown network delay is an economical method. In this paper, we adopt the state-of-the-art matrix completion technology to better estimate the network delay from limited measurements. Although the number of measurements required for an exact matrix completion is theoretically bounded, it is practically less helpful. Therefore, we propose an online adaptive sampling algorithm to measure network delay in which statistical leverage scores are used to select potential matrix elements. The basic principle behind is to sample the elements with larger leverage scores to keep the traits of important rows or columns in the matrix. The amount of samples is adaptively decided by a proposed stopping condition. Simulation results based on real delay matrix show that compared with the traditional sampling algorithm, our proposed sampling algorithm can provide better performance (smaller estimation error and less convergence pressure) at a lower cost (fewer samples and shorter processing time).

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.119-124
• /
• 2015

As Location-Based Services (LBSs) of smartphones increases, the power consumption of a smartphone due to Global Positioning System (GPS) is becoming increasingly serious. This paper presents a new location estimation scheme for smartphones called Adaptive Location Detection (ALD). ALD adaptively detects the location of a smartphone considering the movement pattern of a user, category of applications executed in the smartphone, and the battery level. Simulation with various real applications and scenarios show that ALD reduces 37% of energy consumption compared to GPS. Nevertheless, it satisfies the accuracy requirement of each situation.