• Current Optics and Photonics
• /
• v.8 no.3
• /
• pp.246-258
• /
• 2024

In aerospace and energy engineering, the reconstruction of three-dimensional (3D) temperature distributions is crucial. Traditional methods like algebraic iterative reconstruction and filtered back-projection depend on voxel division for resolution. Our algorithm, blending deep learning with computer graphics rendering, converts 2D projections into light rays for uniform sampling, using a fully connected neural network to depict the 3D temperature field. Although effective in capturing internal details, it demands multiple cameras for varied angle projections, increasing cost and computational needs. We assess the impact of camera number on reconstruction accuracy and efficiency, conducting butane-flame simulations with different camera setups (6 to 18 cameras). The results show improved accuracy with more cameras, with 12 cameras achieving optimal computational efficiency (1.263) and low error rates. Verification experiments with 9, 12, and 15 cameras, using thermocouples, confirm that the 12-camera setup as the best, balancing efficiency and accuracy. This offers a feasible, cost-effective solution for real-world applications like engine testing and environmental monitoring, improving accuracy and resource management in temperature measurement.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.1
• /
• pp.171-184
• /
• 2010

In this work, we develop a novel channel power allocation method for the real-time multimedia over the wireless network environment. Since each frame has different effect on the user perceivable QoS, improving packet loss does not necessarily coincide with perceivable improvements in QoS. A new channel power control scheme is suggested based on the quantified importance of each frame in terms of user perceivable QoS. Dynamic programming formulation is used to obtain optimal transmit power which minimizes power consumption and maximizes user perceivable QoS simultaneously. The experiment is performed by using publicly available video clips. The performance is evaluated using network simulator version 2 (NS 2) and decoding engine is embedded at the client node, and calculated PSNR over the every frame transmitted. Through the semantics aware power allocation (SAPA) scheme, significant improvement on the QoS has been verified, which is the result of unequal protection to more important packets. SAPA scheme reduced the loss of I frame by upto 27% and reduced power consumption by upto 19% without degradation on the user perceivable QoS.

• Journal of Internet Computing and Services
• /
• v.17 no.6
• /
• pp.17-23
• /
• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• Journal of Korea Multimedia Society
• /
• v.6 no.2
• /
• pp.208-215
• /
• 2003

For playing a multimedia presentation in a Internet, the case that the presentation QoS(Quality of Services) at a destination nay be different from the QoS of multimedia data at the source occurs frequently. In this case, the process of trancoding the multimedia data at the source Into the multimedia data satisfying the QoS at the destination should be requited. In addition, even the presentation description having the homogeneous QoS at both sides may have different transcoding paths due to the limitation of display terminals or network bandwidth. That is, for a multimedia description, it is required to regenerate a proper transcoding path whenever the displaying terminals or the network environment gets decided. And the delay time required to go through the transcoding path may affect the playability of the give presentation. Therefore it should be checked whether the presentation requiring a transcoding process is able to be played in a real time. In this paper, the algorithm for generating all the possible transcoding paths for a given multimedia description under a fixed set of transcoders and the network environment is proposed. The algorithm adopts the concept of QoS transition diagram to Prevent from a trancoding Path being cycled by the repetition of a cyclic Path which generates the same QoS of multimedia data as its input QoS. By eliminating all the cyclic Paths, the algorithm can guarantee the termination of the process. And for the playability check, a method of computing the transcoding time and the delay lime between logical data units are proposed.Finally all the proposed methods were implemented in the stream engine, called TransCore and the presentation-authoring tool, called VIP, we had developed. And the test results with sample scenarios were presented at the last.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.279-284
• /
• 2020

In January 2020, COVID19 plunged the whole planet into a pandemic. This has caused great economic losses and is causing social confusion. COVID19 has a superior infection rate among people with underlying disease such as heart disease, high blood pressure, diabetes, stroke, depression, and cancer. In addition, it was studied that patients with underlying disease had a higher fatality rate than those without underlying disease. In this study, the presence or absence of underlying disease was classified using EEG data. The data used to classify the presence or absence of underlying disease was EEG data provided by Data Science lab, consisting of 33 features and 69 samples. Z-score was used for data pretreatment. Classification was performed using the neural network NEWFM and ZNN engine. As a result of the classification of the presence or absence of the underlying disease, the experimental results were 77.945 for NEWFM and 76.4% for ZNN. Through this study, it is expected that EEG data can be measured, the presence or absence of an underlying disease is classified, and those with a high infection rate can be prevented from COVID19. Based on this, there is a need for research that can subdivide underlying disease in the future and research on the effects of each underlying disease on infectious disease.

• Journal of Digital Contents Society
• /
• v.12 no.4
• /
• pp.457-465
• /
• 2011

The annual R&D investment in Korea increased by an average of 12.2percent during the last 5 years. Therefore, prevention of duplicate projects being performed became an important factor in promoting the efficiency of R&D investment and the originality of R&D projects. On measuring the similarity of projects, the measurement model used to estimate the accuracy of the similarity is crucial. In this paper, we propose an advanced measurement model on checking the similarity of R&D projects for promoting the efficiency of R&D investment. The proposed model is made up of the following steps for the model measurement, sampling and analyzing. During the sampling step, we append the abstract of R&D reports on the search engine based on document vector. We then measure the similarity on projects to use research title network which is consists of the compound keyword and the weight of items on during the analysis. The proposed method improved the accuracy for measuring the similarity of projects by an average of 0.19 over the existing search engine and by 9.25 over the simple keyword search on R&D projects. On searching the similarity with the appending conditions and high sampling, it improved the accuracy of measuring the similarity of R&D projects.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.37 no.5
• /
• pp.94-104
• /
• 2000

The accuracy and reliability of the target tracking is very critical issue in the design of automotive collision warning radar A significant problem in multi-target tracking (MTT) is the target-to-measurement data association If an incorrect measurement is associated with a target, the target could diverge the track and be prematurely terminated or cause other targets to also diverge the track. Most methods for target-to-measurement data association tend to coalesce neighboring targets Therefore, many algorithms have been developed to solve this data association problem. In this paper, a new multi-target data association method based on order statistics is described The new approaches. called the order statistics probabilistic data association (OSPDA) and the order statistics joint probabilistic data association (OSJPDA), are formulated using the association probabilities of the probabilistic data association (PDA) and the joint probabilistic data association (JPDA) filters, respectively Using the decision logic. an optimal or near optimal target-to-measurement data association is made A computer simulation of the proposed method in a heavy cluttered condition is given, including a comparison With the nearest-neighbor CNN). the PDA, and the JPDA filters, Simulation results show that the performances of the OSPDA filter and the OSJPDA filter are superior to those of the PDA filter and the JPDA filter in terms of tracking accuracy about 18% and 19%, respectively In addition, the proposed method is implemented using a developed digital signal processing (DSP) board which can be interfaced with the engine control unit (ECU) of car engine and with the d?xer through the controller area network (CAN)

• Journal of KIISE:Databases
• /
• v.37 no.5
• /
• pp.258-266
• /
• 2010

In this paper, we introduce a novel method to predict next update of blogs. The number of RSS feeds registered on meta-blogs is on the order of several million. Checking for updates is very time consuming and imposes a heavy burden on network resources. Since blog search engine has limited resources, there is a fix number of blogs that it can visit on a day. Nevertheless we need to maximize chances of getting new data, and the proposed method which predicts update probability on blogs could bring better chances for it. Also this work is important to avoid distributed denial-of-service attack for the owners of blogs. Furthermore, for the internet as whole this work is important, too, because our approach could minimize traffic. In this study, we assumed that there is a specific pattern to when a blogger is actively posting, in terms of days of the week and, more specifically, hours of the day. We analyzed 15,119 blogs to determine a blogger's posting preference. This paper proposes a method to predict the update probability based on a blogger's posting history and preferred days of the week. We applied proposed method to 12,115 blogs to check the precision of our predictions. The evaluation shows that the model has a precision of 0.5 for over 93.06% of the blogs examined.

• The Journal of the Acoustical Society of Korea
• /
• v.24 no.8
• /
• pp.498-504
• /
• 2005

We introduce a Korean speech recognition platform (ECHOS) developed for education and research Purposes. ECHOS lowers the entry barrier to speech recognition research and can be used as a reference engine by providing elementary speech recognition modules. It has an easy simple object-oriented architecture, implemented in the C++ language with the standard template library. The input of the ECHOS is digital speech data sampled at 8 or 16 kHz. Its output is the 1-best recognition result. N-best recognition results, and a word graph. The recognition engine is composed of MFCC/PLP feature extraction, HMM-based acoustic modeling, n-gram language modeling, finite state network (FSN)- and lexical tree-based search algorithms. It can handle various tasks from isolated word recognition to large vocabulary continuous speech recognition. We compare the performance of ECHOS and hidden Markov model toolkit (HTK) for validation. In an FSN-based task. ECHOS shows similar word accuracy while the recognition time is doubled because of object-oriented implementation. For a 8000-word continuous speech recognition task, using the lexical tree search algorithm different from the algorithm used in HTK, it increases the word error rate by $40\%$ relatively but reduces the recognition time to half.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2007.11a
• /
• pp.95-102
• /
• 2007

This paper presents the core technology of Construction Business Process Automation to model and automate construction business processes. Business Process Reengineering (BPR) and Automation (BPA) have been recognized as one of the important aspects in construction business management. However, BPR requires a lot of efforts to identify, document, implement, execute, maintain, and keep track thousands of business processes to deliver a project. Moreover, existing BPA technologies used in existing Enterprise Resource Planning (ERP) systems do not lend themselves to effective scalability for construction business process management. Application of Workflow and Object Technologies would be quite effective in implementing a scalable enterprise application for construction business processes by addressing how: 1) Automated construction management tasks are developed as software components, 2) The process modeling is facilitated by dragging-and dropping task components in a network, 3) Raising business requests and instantiating corresponding process instances are delivered, and 4) Business process instances are executed by using workflow technology based on real-time simulation engine. This paper presents how the construction business process automation is achieved by using equipment reservation and cancellation processes simplified intentionally.