• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.155-165
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• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.

• Smart Media Journal
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• v.9 no.1
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• pp.23-29
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• 2020

This paper presents an approach for dynamic hand gesture recognition by using algorithm based on 3D Convolutional Neural Network (3D_CNN), which is later extended to 3D Residual Networks (3D_ResNet), and the neural network based key frame selection. Typically, 3D deep neural network is used to classify gestures from the input of image frames, randomly sampled from a video data. In this work, to improve the classification performance, we employ key frames which represent the overall video, as the input of the classification network. The key frames are extracted by SegNet instead of conventional clustering algorithms for video summarization (VSUMM) which require heavy computation. By using a deep neural network, key frame selection can be performed in a real-time system. Experiments are conducted using 3D convolutional kernels such as 3D_CNN, Inflated 3D_CNN (I3D) and 3D_ResNet for gesture classification. Our algorithm achieved up to 97.8% of classification accuracy on the Cambridge gesture dataset. The experimental results show that the proposed approach is efficient and outperforms existing methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.964-973
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• 2015

Ship Area Network(SAN) can monitor the status of ship in real time and minimize the maintenance costs by connecting various devices to the network. In particular, among researches on SAN, Wireless Sensor Network using sensor nodes that is low-cost, low-power, and multifunctional has a number of advantages. In this paper, we propose cluster-based multi-channel algorithm considering the energy efficiency in wireless sensor network in a ship. The proposed algorithm shows the result of improvement of throughput and energy efficiency, because it reduces interference between clusters by using channel allocation algorithm that is distributed and dynamic.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.59-67
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• 2013

Compared to the conventional Hardware-oriented base stations, Software Defined Radio (SDR)-based base station provides various advantages especially in flexibility and expandability. It enables the multimode capability required in 4th-generation (4G) environment which aims at a convergence network of various kinds of communication standards. However, since a single base station processes all data required in various multiple waveforms, the SDR base station faces a problem of data processing speed. In this paper, we propose a new concept of SDR base station system which adopts a parallel processing technology of clustering environment. We implemented a WiMAX system with SDR concept which adopts the Message Passing Interface (MPI) technology which enables the speed-up operations. In order to maximize the efficiency of parallel processing in signal processing, we analyze how the algorithm at each of modules is related to data to be processed. Through the implemented system, we show a drastic improvement in operation time due to parallel processing using the proposed MPI technology. In addition, we demonstrate a feasibility of SDR system for 4G or even beyond-4G as well.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.141-150
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• 2012

The present study examines an efficient congestion management system compatible with the evolving environment. The key is to build an information model shared and exchanged for marketbased solutions to alleviate congestion. Traditional methods for congestion management can be classified into two categories, i.e., the centralized scheme and the decentralized scheme, depending on the extent to which the independent system operator (ISO) is involved in market participants' (MPs) activities. Although the centralized scheme is more appropriate for providing reliable system operation and relieving congestion in near real-time, the decentralized scheme is preferred for supporting efficient market operation. The minimum set of information between the ISO and MPs for decentralized scheme is identified: i) congestion-based zone, ii) Power Transfer Distribution Factors, and iii) transmission congestion cost. The mathematical modeling of the proposed information is expressed, considering its process of making effective use of information. Numerical analysis is conducted to demonstrate both cost minimization from the MP perspective and the reliability enhancement from the ISO perspective based on the proposed information exchange scheme.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1778-1786
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• 2009

Many sensor network applications have been developed for smart home, disaster management, and a wide range of other applications. These applications, however, generally assume a fixed base station as well as fixed sensor nodes. Previous research on sensor networks mainly focused on efficient transmission of data from sensors to fixed sink nodes. Recently there has been active research on mobile sink nodes, sink mobility is one of the most comprehensive trends for information gathering in sensor networks, but the research of an environment where both fixed sink nodes and mobile sinks are present at the same time is rather scarce. This paper proposes a scheme for context-aware by ubiquitous devices with the sink functionality added through fixed sinks under a previously-built, cluster-based multi-hop sensor network environment. To this end, clustering of mobile devices were done based on the fixed sinks of a previously-built sensor network, and by using appropriate fixed sinks, context gathering was made possible. By mathematical comparison with TTDD routing protocol, which was proposed for mobile sinks, it was confirmed that performance increases by average 50% in energy with the number of mobile sinks, and with the number of movements by mobile devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2115-2130
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• 2022

With the development of over-the-top (OTT) services, the demand for content is increasing, and you can easily and conveniently acquire various content in the online environment. As a result, copyrighted content can be easily copied and distributed, resulting in serious copyright infringement. Some special forms of online service providers (OSP) use filtering-based technologies to protect copyrights, but illegal uploaders use methods that bypass traditional filters. Uploading with a title that bypasses the filter cannot use a similar search method to detect illegal content. In this paper, we propose a technique for profiling the Heavy Uploader by normalizing the bypassed content title and efficiently detecting illegal content. First, the word is extracted from the normalized title and converted into a bit-array to detect illegal works. This Bloom Filter method has a characteristic that there are false positives but no false negatives. The false positive rate has a trade-off relationship with processing performance. As the false positive rate increases, the processing performance increases, and when the false positive rate decreases, the processing performance increases. We increased the detection rate by directly comparing the word to the result of increasing the false positive rate of the Bloom Filter. The processing time was also as fast as when the false positive rate was increased. Afterwards, we create a function that includes information about overall piracy and identify clustering-based heavy uploaders. Analyze the behavior of heavy uploaders to find the first uploader and detect the source site.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.413-435
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• 2018

Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Design of an unsupervised apriori algorithm for intra-class clustering, making the subsequent network training more pertinent; (2) presentation of an intermediate convolution model with a pre-processed width level of 2;(3) presentation of a composite residual structure that designs a multi-level quick link; and (4) addition of a Dropout layer after the parameter optimization. The algorithm shows superior results on MNIST and SVHN dataset, which are two character benchmark recognition datasets, and achieves better recognition accuracy and higher recognition efficiency than other deep structures with the same number of layers.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.175-182
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• 2008

Context-aware service environment should provide many kinds of services according to users' requests. Users want a great variety of services. In response to their demands the service provider should make a new service every time. But making a new service every time may be inefficient even for a small number of users' requests. So, there are studies on how to efficiently support various and complex requests fFom users. In many researches, service compositions have lately attracted considerable attention. However, existing researches have mainly focused on Web services. So they are not proper to rapidly providing services in response to users' requests, especially In context-aware service environment. This paper proposes a rapid service composition using case-based reasoning. For evaluating the proposed algorithm we implement 'purchasing seTvice agent'. With this system, we compare our algorithm and the existing service composition algorithms.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.716-725
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• 2004

It is extremely important to minimize network access time in constructing a high-performance PC cluster system. For PC cluster systems, it is possible to reduce network access time by maintaining network cache in each cluster node. This paper presents a Network Cache Coherent NUMA (NCC-NUMA) system to utilize network cache by locating shared memory on the PCI bus, and the NCC-NUMA card which is core module of the NCC-NUMA system is developed. The NCC-NUMA card is directly plugged into the PCI slot of each node, and contains shared memory, network cache, shared memory control module and network control module. The network cache is maintained for the shared memory on the PCI bus of cluster nodes. The coherency mechanism between the network cache and the shared memory is based on the IEEE SCI standard. According to the SPLASH-2 benchmark experiments, the NCC-NUMA system showed improvements of 56% compared with an SCI-based cluster without network cache.