• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.368-376
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• 2021

Artificial intelligence clouds help to efficiently develop the autonomous things integrating artificial intelligence technologies and control technologies by sharing the learned models and providing the execution environments. The existing autonomous things development technologies only take into account for the accuracy of artificial intelligence models at the cost of the increment of the complexity of the models including the raise up of the number of the hidden layers and the kernels, and they consequently require a large amount of computation. Since resource-constrained computing environments, could not provide sufficient computing resources for the complex models, they make the autonomous things violate time criticality. In this paper, we propose a digital twin software development framework that selects artificial intelligence models optimized for the computing environments. The proposed framework uses a load estimation DNN model to select the optimal model for the specific computing environments by predicting the load of the artificial intelligence models with digital twin data so that the proposed framework develops the control software. The proposed load estimation DNN model shows up to 20% of error rate compared to the formula-based load estimation scheme by means of the representative CNN models based experiments.

• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.247-257
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• 2021

This paper proposes a new technique for calibrating a multi-view RGB-D camera using a 3D (dimensional) skeleton. In order to calibrate a multi-view camera, consistent feature points are required. In addition, it is necessary to acquire accurate feature points in order to obtain a high-accuracy calibration result. We use the human skeleton as a feature point to calibrate a multi-view camera. The human skeleton can be easily obtained using state-of-the-art pose estimation algorithms. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D skeleton obtained through the posture estimation algorithm as a feature point. Since the human body information captured by the multi-view camera may be incomplete, the skeleton predicted based on the image information acquired through it may be incomplete. After efficiently integrating a large number of incomplete skeletons into one skeleton, multi-view cameras can be calibrated by using the integrated skeleton to obtain a camera transformation matrix. In order to increase the accuracy of the calibration, multiple skeletons are used for optimization through temporal iterations. We demonstrate through experiments that a multi-view camera can be calibrated using a large number of incomplete skeletons.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.157-164
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• 2023

This paper suggests O(n²) polynomial time heuristic algorithm for corporate tax structure optimization problem that has been classified as NP-complete problem. The proposed algorithm constructs tax tree levels that the target holding company is located at root node of Level 1, and the tax code categories(T_e) 1,4,3,2 are located in each level 2,3,4,5 sequentially. To find the maximum tax-relief path from source(S) to target(T), firstly we connect the minimum witholding tax rate _minr_w(u, v) arc of node u point of view for transfer the profit from u to v node. As a result we construct the spanning tree from all of the source nodes to a target node, and find the initial feasible solution. Nextly, we find the alternate path with minimum foreign tax rate _minr_fi(u, v) of v point of view. Finally we choose the minimum tax-relief path from of this two paths. The proposed heuristic algorithm performs better optimal results than linear programming and Tabu search method that is a kind of metaheuristic method.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.31-43
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• 2022

Traditional video compression has developed so far based on hybrid compression methods through motion prediction, residual coding, and quantization. With the rapid development of technology through artificial neural networks in recent years, research on image compression and video compression based on artificial neural networks is also progressing rapidly, showing competitiveness compared to the performance of traditional video compression codecs. In this paper, a new method capable of improving the performance of such an artificial neural network-based video compression model is presented. Basically, we take the rate-distortion optimization method using the auto-encoder and entropy model adopted by the existing learned video compression model and shifts some components of the latent information that are difficult for entropy model to estimate when transmitting compressed latent representation to the decoder side from the encoder side, and finally compensates the distortion of lost information. In this way, the existing neural network based video compression framework, MFVC (Motion Free Video Compression) is improved and the BDBR (Bjøntegaard Delta-Rate) calculated based on H.264 is nearly twice the amount of bits (-27%) of MFVC (-14%). The proposed method has the advantage of being widely applicable to neural network based image or video compression technologies, not only to MFVC, but also to models using latent information and entropy model.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.3-12
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• 2022

Image deblurring aims to remove image blur, which can be generated while shooting the pictures by the movement of objects, camera shake, blurring of focus, and so forth. With the rise in popularity of smartphones, it is common to carry portable digital cameras daily, so image deblurring techniques have become more significant recently. Originally, image deblurring techniques have been studied using traditional optimization techniques. Then with the recent attention on deep learning, deblurring methods based on convolutional neural networks have been actively proposed. However, most of them have been developed while focusing on better performance. Therefore, it is not easy to use in real situations due to the speed of their algorithms. To tackle this problem, we propose a novel deep learning-based deblurring algorithm that can be operated in real-time on HD resolution. In addition, we improved the training and inference process and could increase the performance of our model without any significant effect on the speed and the speed without any significant effect on the performance. As a result, our algorithm achieves real-time performance by processing 33.74 frames per second at 1280×720 resolution. Furthermore, it shows excellent performance compared to its speed with a PSNR of 29.78 and SSIM of 0.9287 with the GoPro dataset.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.6
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• pp.583-591
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• 2015

In order to evaluate the performance of TAS applied to the hybrid vehicle of the soft belt driven, acceleration performance and fuel consumption performance is to be superior to the existing vehicle. The key components of belt driven TAS(Torque Assist System), such as the engine, the motor and the battery, The key components of the driven belt TAS, such as the engine, the motor, and the battery, have a significant impact on fuel consumption performance of the vehicle. Therefore, in order to improve the efficiency at the point of view based on the overall system, the study of the power distribution algorithm for controlling the main source powers is necessary. In this paper, we propose the power distribution algorithm, applied the homogeneous analysis method in terms of fuel equivalent, for minimizing the fuel consumption. We have confirmed that the proposed algorithm is contribute to improving the fuel consumption performance satisfied the constraints considering the vehicle status information and the required power through the control parameters to minimize the fuel consumption of the engine. The optimization process of the proposed driving strategy can reduce the trial and error in the research and development process and monitor the characteristics of the control parameter quickly and accurately. Therefore, it can be utilized as a way to derive the operational strategy to minimize the fuel consumption.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.37-42
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• 2024

This paper suggests polynomial time algorithm for cutwidth minimization problem that classified as NP-complete because the polynomial time algorithm to find the optimal solution has been unknown yet. To find the minimum cutwidth CW_f(G)=max_𝜈∈_VCW_f(𝜈)for given graph G=(V,E),m=｜V｜, n=｜E｜, the proposed algorithm divides neighborhood N_G[𝜈_i] of the maximum degree vertex 𝜈_i in graph G into left and right and decides the vertical cut plane with minimum number of edges pass through the vertex 𝜈_i firstly. Then, we split the left and right N_G[𝜈_i] into horizontal sections with minimum pass through edges. Secondly, the inner-section vertices are connected into line graph and the inter-section lines are connected by one line layout. Finally, we perform the optimization process in order to obtain the minimum cutwidth using vertex moving method. Though the proposed algorithm requires O(n²) time complexity, that can be obtains the optimal solutions for all of various experimental data

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.37-57
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• 2018

With the spread of smart phones, interest in mobile media is on the increase as useful media recently. Mobile media is assessed as having differentiated advantages from existing media in that not only can they provide consumers with desired information anytime and anywhere but also real-time interaction is possible in them. So far, studies on mobile advertising were mostly researches analyzing satisfaction with, and acceptance of, mobile advertising based on survey, researches focusing on the factors affecting acceptance of mobile advertising messages and researches verifying the effect of mobile advertising on brand recall, advertising attitude and brand attitude through experiments. Most of the domestic mobile advertising studies related to advertisement effect and advertisement attitude have been conducted through experiments and surveys. The advertising effectiveness measure of the mobile ad used the attitude of the advertisement, purchase intention, etc. To date, there have been few studies on the effects of mobile advertising on actual advertising data to prove the characteristics of the advertising platform and to prove the relationship between the factors influencing the advertising effect and the factors. In order to explore advertising effect of mobile advertising platform currently commercialized, this study defined advertising characteristics and media characteristics from the perspective of advertiser, advertising platform and publisher and analyzed the influence of each characteristic on advertising effect. As the advertisement characteristics, we classified advertisement format classified by bar type and floating type, and advertisement material classified by image and text. We defined advertisement characteristics of advertisement platform as Hedonic and Utilitarian media characteristics. As a dependent variable, we use CTR, which is the ratio of response (click) to ad exposure. The theoretical background and the analysis of the mobile advertising business, the hypothesis that the advertisement effect is different according to the advertisement specification, the advertisement material, In the ad standard, bar ads are classified as static framing, Floating ads can be categorized as dynamic framing, and the hypothetical definition of floating advertisements, which are high-profile dynamic framing ads, is highly responsive. In advertising, images with high salience are defined to have higher ad response than text. In the media characteristics classified as practical / hedonic type, it is defined that the hedonic type media has a more relaxed tendency than the practical media, and there is a high possibility of receiving various information because there is no clear target. In addition, image material and hedonic media are defined to be highly effective in the interaction between advertisement specification and advertisement material, advertisement specifications and media characteristics, and advertisement material and media characteristics. As the result of regression analysis on each characteristic, material standard, which is a characteristic of mobile advertisement, and media characteristics separated into 'Hedonic' and 'Utilitarian' had significant influence on advertisement effect and mutual interaction effect was also confirmed. In the mobile advertising standard, the advertising effect of the floating advertisement is higher than that of the bar advertisement, Floating ads were more effective than text ads for image ads. In addition, it was confirmed that the advertising effect is higher in the practical media than the hedonic media. The research was carried out with the big data collected from the mobile advertising platform, and it was possible to grasp the advertising effect of the measure index standard which is used in the practical work which could not be grasped in the previous research. In other words, the study was conducted using the CTR, which is a measure of the effectiveness of the advertisement used in the online advertisement and the mobile advertisement, which are not dependent on the attitude of the ad, the attitude of the brand, and the purchase intention. This study suggests that CTR is used as a dependent variable of advertising effect based on actual data of mobile ad platform accumulated over a long period of time. The results of this study is expected to contribute to establishment of optimum advertisement strategy such as creation of advertising materials and planning of media which suit advertised products at the time of mobile advertisement.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1332-1344
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• 2004

Upon development of information super-highway and multimedia-related technoiogies in recent years, more efficient technologies to transmit, store and retrieve the multimedia data are required. Among such technologies, firstly, it is common that the semantic-based image retrieval is annotated separately in order to give certain meanings to the image data and the low-level property information that include information about color, texture, and shape Despite the fact that the semantic-based information retrieval has been made by utilizing such vocabulary dictionary as the key words that given, however it brings about a problem that has not yet freed from the limit of the existing keyword-based text information retrieval. The second problem is that it reveals a decreased retrieval performance in the content-based image retrieval system, and is difficult to separate the object from the image that has complex background, and also is difficult to extract an area due to excessive division of those regions. Further, it is difficult to separate the objects from the image that possesses multiple objects in complex scene. To solve the problems, in this paper, I established a content-based retrieval system that can be processed in 5 different steps. The most critical process of those 5 steps is that among RGB images, the one that has the largest and the smallest background are to be extracted. Particularly. I propose the method that extracts the subject as well as the background by using an Image, which has the largest background. Also, to solve the second problem, I propose the method in which multiple objects are separated using RGB channel selection techniques having optimized the excessive division of area by utilizing Watermerge's threshold value with the object separation using the method of RGB channels separation. The tests proved that the methods proposed by me were superior to the existing methods in terms of retrieval performances insomuch as to replace those methods that developed for the purpose of retrieving those complex objects that used to be difficult to retrieve up until now.