• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1134-1149
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• 2013

Pub/sub system enables data users to access any necessary data without knowledge of the data producer and synchronization with the data producer. It is widely used as the middleware technology for the data-centric services. DDS (Data Distribution Service) is a standard middleware supported by the OMG (Object Management Group), one of global standardization organizations. It is considered quite useful as a standard middleware for US military services. However, it is well-known that it takes considerably long time in searching the Participants and Endpoints in the system, especially when the system is booting up. In this paper, we propose a discovery scheme to reduce the latency when the participants and Endpoints are densely distributed in a small area. We propose to modify the standard DDS discovery process in three folds. First, we integrate the Endpoint discovery process with the Participant discovery process. Second, we reduce the number of connections per participant during the discovery process by adopting the concept of successors in Distributed Hashing scheme. Third, instead of UDP, the participants are connected through TCP to exploit the reliable delivery feature of TCP. We evaluated the performance of our scheme by comparing with the standard DDS discovery process. The evaluation results show that our scheme achieves quite lower discovery latency in case that the Participants and the Endpoints are densely distributed in a local network.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.83-90
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• 2004

DVB-MHP recommends that NPT(normal play time) be used as the times of stream events. NPT is the local time within an event(TV program). But we found that commercial transport stream (TS) generators and middlewares for DVB-MHP settop boxes are not ready to support the use of NPT by applications. In particular, TS generators do not create NPT reference descriptors needed to reconstruct NPT at the TV receiver. This situation is undesirable because program providers cannot experiment with the idea of synchronized applications. So we have implemented a TS generator that inserts NPT reference descriptors to TS and MyGetNPT API to approximately reconstruct NPT. STC (system time clock) is needed to reconstruct NPT, but Xlets are not allowed to read it. So, we approximate STC by using PCR (program clock reference) and the Java system tune. In this method, the stream generator extrats PCRs from an existing TS and inserts them into null TS packets in the form of MPEG sections, which can be read by Xlets. Because PCRs are displaced into new positions in TS, their values should be adjusted based on the time intervals between the original positions and the new positions. We implemented a synchronized application by using our TS generator and MyGetNPT API, where the task of stream events are to display graphic images. We found that graphic images are displayed where 240 ㎳ from their intended time, where 240ms is a human tolerance for the synchronization skew between graphic image and video.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.7
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• pp.421-428
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• 2006

In this paper, the way of remote installation and back-up of 3-tier structure is introduced for efficient utilizing the cluster system resources distributed at several places. Recently, cluster system is constructed as the system of over hundreds nodes under complex network system mixed with public networks and private networks. Therefore, the as installation method suitable for the large scale cluster system and the remote recovery of failure nodes are important. However the previous researches which are based on 2-tier architecture may not provide the efficient cluster installation and image back-up method when the network of cluster system is composed of several private networks and public networks. In this paper, RISE (Remote Installation Service and Environment) based on the 3-tier architecture is proposed to solve this problem. In our approach, the managing node's role is divided into the global master node (GRISE) and the local master node (LRISE) to provide the efficient initial system deployment and remote failure recovery of distributed cluster system under the various network systems. Also, LRISE's availability is ensured under the complex network environments by adopting the auto-synchronization mechanism between GRISE and LRISE. In this work, a 64-node cluster system with gigabit network system is utilized for the experiment. From the experimental result, the system image with 1.86GB data can be obtained in 5 minutes and 53 seconds and the image-based installation of 64-node system can be carried out in 17 minutes and 53 seconds.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.