• Smart Structures and Systems
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• v.29 no.1
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• pp.141-151
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• 2022

In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.2
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• pp.27-32
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• 2018

One of the crucial issues in mobile edge cloud computing environments is to monitor mobile devices. Due to the inherit properties of mobile devices, they are prone to unstable behavior that leads to failures. In order to satisfy the service level agreement (SLA), the mobile edge cloud administrators should take appropriate measures through a monitoring scheme. In this paper, we propose a monitoring scheme of mobile devices based on artificial intelligence in mobile edge cloud computing environments. The proposed monitoring scheme is able to measure faults of mobile devices based on previous and current monitoring information. To this end, we adapt the hidden markov chain model, one of the artificial intelligence technologies, to monitor mobile devices. We validate our monitoring scheme based on the hidden markov chain model. The proposed monitoring scheme can also be used in general cloud computing environments to monitor virtual machines.

• Korea Science and Art Forum
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• v.34
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• pp.251-262
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• 2018

The purpose of this study is to understand how wearable computing devices are designed and how to design them in a technology based wearable device design research. Research is premised on the consideration of producers and consumers. There is wearable computer of eyeglasses, watches, clothes, and so on. The user can always wear these products comfort and use as part of the body without any sense of discomfort, and the goal is to supplement or double the ability of the human being. It should be easy to use them convenient, wear comfortable, safe and sociable at any time. For the satisfaction these conditions, the wearable computing devices have several factors. There are technical performances, visual aesthetics, Human body system and devices communication and safety. Furthermore, these factors have to match to operating system, real-time operating system and applied software. To comprehend wearable computing devices should be offered the design of the both software and hardware designed.

• Journal of information and communication convergence engineering
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• v.13 no.1
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• pp.15-20
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• 2015

The limited battery power in the mobile environment, lack of sufficient wireless bandwidth, limited resources of mobile terminals, and frequent breakdowns of the wireless network have become major hurdles in the development of mobile cloud computing (MCC). In order to solve the abovementioned problems, This paper propose a proxy-based MCC framework by adding a proxy server between mobile devices and cloud services to optimize the access to cloud services by mobile devices on the network transmission, application support, and service mode levels. Finally, we verify the effectiveness of the developed framework through an experimental analysis. This framework can ensure that mobile users have efficient access to cloud services.

• Journal of Computing Science and Engineering
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• v.9 no.3
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• pp.155-162
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• 2015

The last decade has seen a rapid growth in the use of mobile devices all over the world. With an increasing use of mobile devices, mobile applications are becoming more diverse and complex, demanding more computational resources. However, mobile devices are typically resource-limited (i.e., a slower-speed CPU, a smaller memory) due to a variety of reasons. Mobile users will be capable of running applications with heavy computation if they can offload some of their computations to other places, such as a desktop or server machines. However, mobile users are typically subject to dynamically changing network environments, particularly, due to user mobility. This makes it hard to choose good offloading decisions in mobile environments. In general, users' mobility can provide some hints for upcoming changes to network environments. Motivated by this, we propose a mobility model of each individual user taking advantage of the regularity of his/her mobility pattern, and develop an offloading decision-making technique based on the mobility model. We evaluate our technique through trace-based simulation with real log data traces from 14 Android users. Our evaluation results show that the proposed technique can help boost the performance of mobile devices in terms of response time and energy consumption, when users are highly mobile.

• Smart Media Journal
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• v.11 no.3
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• pp.18-25
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• 2022

Neuromorphic computing generally shows significantly better power, area, and speed performance than neural network computation using CPU and GPU. These characteristics are suitable for resource-constrained IoT environments where energy consumption is important. However, there is a problem in that it is necessary to modify the source code for environment setting and application operation according to heterogeneous IoT devices that support neuromorphic computing. To solve these problems, NAAL was proposed and implemented in this paper. NAAL provides functions necessary for IoT device control and neuromorphic architecture abstraction and inference model operation in various heterogeneous IoT device environments based on common APIs of NAAL. NAAL has the advantage of enabling additional support for new heterogeneous IoT devices and neuromorphic architectures and computing devices in the future.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.497-497
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• 2006

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.253-253
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• 2004

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5375-5400
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• 2016

IOMMU is a hardware unit that is indispensable for DMA. Besides address translation and remapping, it also provides I/O virtual address space isolation among devices and memory access control on DMA transactions. However, currently commodity virtualization platforms lack of IOMMU virtualization, so that the virtual machines are vulnerable to DMA security threats. Previous works focus only on DMA security problem of directly assigned devices. Moreover, these solutions either introduce significant overhead or require modifications on the guest OS to optimize performance, and none can achieve high I/O efficiency and good compatibility with the guest OS simultaneously, which are both necessary for production environments. However, for simulated virtual devices the DMA security problem also exists, and previous works cannot solve this problem. The reason behind that is IOMMU circuits on the host do not work for this kind of devices as DMA operations of which are simulated by memory copy of CPU. Motivated by the above observations, we propose an IOMMU para-virtualization solution called PVIOMMU, which provides general functionalities especially DMA security guarantees for both directly assigned devices and simulated devices. The prototype of PVIOMMU is implemented in Qemu/KVM based on the virtio framework and can be dynamically loaded into guest kernel as a module, As a result, modifying and rebuilding guest kernel are not required. In addition, the device model of Qemu is revised to implement DMA access control by separating the device simulator from the address space of the guest virtual machine. Experimental evaluations on three kinds of network devices including Intel I210 (1Gbps), simulated E1000 (1Gbps) and IB ConnectX-3 (40Gbps) show that, PVIOMMU introduces little overhead on DMA transactions, and in general the network I/O performance is close to that in the native KVM implementation without IOMMU virtualization.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.77-83
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• 2022

In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.