• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.119-124
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• 2023

Deep learning has recently demonstrated conspicuous efficacy across diverse domains than traditional machine learning techniques, as the most popular approach for pattern recognition. The classification problems for tabular data, however, are remain for the area of traditional machine learning. This paper introduces a novel network module designed to tabular data into high-dimensional tensors. The module is integrated into conventional deep learning networks and subsequently applied to the classification of structured data. The proposed method undergoes training and validation on four datasets, culminating in an average accuracy of 90.22%. Notably, this performance surpasses that of the contemporary deep learning model, TabNet, by 2.55%p. The proposed approach acquires significance by virtue of its capacity to harness diverse network architectures, renowned for their superior performance in the domain of computer vision, for the analysis of tabular data.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.163-172
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• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

• Journal of Technology Innovation
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• v.25 no.3
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• pp.1-24
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• 2017

With the rise of internet of things (IoT), there have been several studies to analyze the technological trend and technological convergence. However, previous work have been relied on the qualitative work that investigate the IoT trend and implication for future business. In response, this study considers the patent information as the proxy measure of technology, and conducts a quantitative and analytic approach for analyzing technological convergence using patent co-classification analysis and text mining. First, this study investigate the characteristics of IoT business, and characterize IoT business into four dimensions: device, network, platform, and services. After this process, total 923 patent classes are classified into four types of IoT technology group. Since most of patent classes are classified into device technology, we developed a co-classification network for both device technology and all technologies. Patent keywords are also extracted and these keywords are also classified into four types: device, network, platform, and services. As a result, technologies for several IoT devices such as sensors, healthcare, and energy management are derived as a main convergence group for the device network. For the total IoT network, base network technology plays a key role to characterize technological convergence in the IoT network, mediating the technological convergence in each application area such as smart healthcare, smart home, and smart grid. This work is expected to effectively be utilized in the technology planning of IoT businesses.

• Journal of Korea Multimedia Society
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• v.5 no.1
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• pp.10-17
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• 2002

It is necessary to induce for using mass transit instead of passenger car, which is high occupied roadway. It is necessary to develop the automated enforcement system to do manage such things. There are lots of problems to enforce the exclusive roadway. One of the biggest problem is the difficulty of vehicle classification. In this paper, morphological vehicle classification algorithm is proposed. Vehicle object is separated from background using frame difference, then the proposed unique weighted skeleton feature is extracted. The experiments show that the vehicle identification results produced by weighted skeleton feature seem to be good quality.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.184-191
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• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.235-241
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• 2015

Nowadays, research and industry on the internet of things is rapidly developing. Bug fixed field of the Software development related internet of things is a very important things. In this study, we analyze the properties that can affect what the bug fix-time by analyzing the time required to fix a bug associated with the Internet of Things. Using the k-NN classification method based on the attribute information to be classified as bug reports. Extracts a bug report based on the results of a similar property. Bug fixed by calculating the time of a similar bug report predicts the fix-time for new bugs. Depending on the prediction of the properties that affect the bug correction time, the properties of os, component, reporter, and assignee showed the best prediction accuracy.

• Hwankyungkyoyuk
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• v.20 no.1
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• pp.19-27
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• 2007

This study was intended to answer the question, 'What kinds of effects will be aroused by experiential learning for environment based on living area?'. Experiential learning for environment was operated to 17 elementary school students in 4th grade in Kyeong-san city. The results were drawn analyzing the mind map for the changes of environmental consciousness before and after learning, and they are as below. First, it had an effect to change the meaning association of the relationship between 'river and me'. Meaning association was 'river-a thing' before experiential learning, but it was developed as 'river-a thing-me' after learning. This means that students expanded understanding of the world that they were belonging and self-spatialization was promoted. The expansion of meaning association would be a start point and a method to promote their segmentation for each student. Second, students could self-directly modify misconception and preconception after experiential learning. It showed that students could find meanings in the world that they were belonging by experiential learning for environment, and misconception obtained by concept learning without actual situation could be revised through the truth recognition in meanings, and student could see what things displayed. Therefore preconception would be corrected. Of course, everything would not be completed by just one time of experiential learning, and consistent experience learning should be operated. Third, experiential learning promoted the change of sensitivity. Students had shallow sensitivity, which appeared in the relation with things, since having learned only inside of class without a direct observation. However their sensitivity could be increased by experiencing specific things. Fourth, there was the change of classification recognition. Students found properties of things with a direct observation. It raised their ability to classify things, and to understand an individual thing in 'a class'.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.306-311
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• 2015

This study aims at analyzing the convergence of Internet-of-Things and wearables technologies using cooperative patent classification(CPC). CPC, introduced to an increasing number of technological fields of Korean patents, is expected to be widely used in Patent Informatics because the classification codes in CPC are more specific than those of IPC, which reflect the characteristics of technologies in detail with accuracy. CPC has seldom been used up to date and most of the previous researches on technological convergence used IPC. As a pre-analysis step for analyzing the trend of technological convergence of IoT and wearables, CPC and IPC codes assigned to each patent were compared. By applying association rule mining to the analysis of CPC codes, we identified the technological fields where convergence frequently takes place and examined the trend of technological convergence over time.