• Journal of IKEEE
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• v.25 no.1
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• pp.148-155
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• 2021

As robot technology advances, research on the driving system of mobile robots is actively being conducted. The driving system of a mobile robot configured based on two-wheels and four-wheels has an advantage in unidirectional driving such as a straight line, but has disadvantages in turning direction and rotating in place. A ball robot using a ball as a wheel has an advantage in omnidirectional movement, but due to its structurally unstable characteristics, balancing control to maintain attitude and driving control for movement are required. By estimating the position from an encoder attached to the motor, conventional ball robots have a limitation, which causes the accumulation of errors during driving control. In this study, a driving control system was proposed that estimates the position coordinates of a ball robot through image processing and uses it for driving control. A driving control system including an image processing unit, a communication unit, a display unit, and a control unit for estimating the position of the ball robot was designed and manufactured. Through the driving control experiment applying the driving control system of the ball robot, it was confirmed that the ball robot was controlled within the error range of ±50.3mm in the x-axis direction and ±53.9mm in the y-axis direction without accumulating errors.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.91-98
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• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.327-335
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• 2021

The purpose of this study is to find out the use and inconvenience of informatization devices according to the disability of the elderly. A total 10,097 data from the elderly survey were used to analyze the differences in the possession and use of information devices, usage capabilities, and injustice according to disability. Descriptive analysis was conducted to find out general characteristics and disability-related characteristics. chi-square tests were conducted to find out the difference in informatization and communication device usage ability and information device usage inconvenience according to disability. The p-value was set at 0.001. As a result of the study, the elderly with disabilities in Korea had lower smartphones and tablet PCs than the non-disabled elderly. In terms of daily inconvenience, the elderly with disabilities often responded that they had no experience or did not know about the inconvenience of online reservations for trains/high-speed buses/intercity buses, ordering kiosks at restaurants, using ATMs, or reducing bank stores. Taken together, the ability of the elderly with disabilities in Korea to possess and use informatization devices is very low and they feel more inconvenience than the non-disabled elderly. It is necessary to improve the ability to possess and use informatization devices for the elderly with disabilities in the rapidly progressing aging of the disabled and the informatization of our society.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.147-156
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• 2022

Industrial control systems that manage and maintain various industries were mainly operated in closed environment without external connection, but with the recent development of the Internet and the introduction of ICT technology, the access to the industrial control system of external or attackers has become easier. Such incorrect approaches or attacks can undermine the availability, a major attribute of the industrial control system, and violation of availability can cause great damage. In this paper, when issuing commands in an industrial control system, a verification group is formed using a random list to verify and execute commands, and a trust score technique is introduced that applies feedback to the verification group that conducted verification using the command execution result. This technique can reduce overhead generated by random generation in the process of requesting command verification, give flexibility to the verification process, and ensure system availability. For the performance analysis of the system, we measured the time and gas usage when deploying a smart contract, gas usage when verifying a command. As a result, we confirmed that although the proposed system generates a random list compared to the legacy system, there was little difference in the time when it took to deploy smart contract and that the gas used to deploy smart contract increased by about 1.4 times in the process of generating a random list. However, the proposed system does not perform random operations even though the operation of command verification and confidence score technique is performed together during the command verification process, thus it uses about 9% less gas per verification, which ensures availability in the verification process.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.1-13
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• 2022

As the information and communication environment develops, the environment of military facilities is also development remarkably. In proportion to this, cyber threats are also increasing, and in particular, APT attacks, which are difficult to prevent with existing signature-based cyber defense systems, are frequently targeting military and national infrastructure. It is important to identify attack groups for appropriate response, but it is very difficult to identify them due to the nature of cyber attacks conducted in secret using methods such as anti-forensics. In the past, after an attack was detected, a security expert had to perform high-level analysis for a long time based on the large amount of evidence collected to get a clue about the attack group. To solve this problem, in this paper, we proposed an automation technique that can classify an attack group within a short time after detection. In case of APT attacks, compared to general cyber attacks, the number of attacks is small, there is not much known data, and it is designed to bypass signature-based cyber defense techniques. As an attack model, we used MITRE ATT&CK® which modeled many parts of cyber attacks. We design an impact score considering the versatility of the attack techniques and proposed a group similarity score based on this. Experimental results show that the proposed method classified the attack group with a 72.62% probability based on Top-5 accuracy.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.119-126
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• 2023

Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developed countries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by the European Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently, it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing test in addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body composition analysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle mass using abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model based on abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat by selecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed by calculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitals are shown. Based on the verification results, we tried to review and supplement the problems and solutions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.129-136
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• 2008

Along with the development in cutting-edge digital technology, the street space is also being changed. Mobile telecommunication units and internet give a big change in a human being's lifestyle. And the ubiquitous computing is proceeding with expanding its application range from the indoor space to the street space. As the street furniture is the convenient facility that allows a human being's life in street space to be abundant, it is getting advanced. First of all, in terms of such phenomenon, this study analyzed the cases of a research on application of street space and the actual condition of a change in the number of individuals for the street furniture, through a literature research of ubiquitous. Also, it researched into the realities of using the street furniture of the walking-desired streets at Daehac-ro and Hongdae district, where are two representative places related to digital generation. The next was carried out FGI (Focus Group Interview) with users of the street space in front of Hongik University and managers of the street furniture, and was researched into the use & management behavior, and recognition level on the street furniture. Thus, the key elements were extracted such as interchange of information for cultural activities, automation for interaction variability in function. Finally the core elements for future vision of street furniture in this digital era were extracted in 3I, namely, Information, Intellectualization, and Integration. This is considered to be applied to the establishment of direction in the process of high-tech digitalization in street furniture related to information hereafter.