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Memory Model Design for Integer-Pointer Casting Support in C-like languages Via Dual Non-determinism
http://doi.org/10.5626/JOK.2024.51.7.643
In system programming, pointers are essential elements. However, applying formal verification methods to programs involving integer-pointer casting poses an important challenge. To address this challenge, a mathematically defined memory model that supports integer-pointer casting, along with proof techniques for verification, is necessary. This study presents a memory model that supports integer-pointer casting within the Coq proof assistant. The model accommodates patterns associated with integer-pointer operations, including one-past-the-end pointers. Additionally, a simulation-based proof technique is introduced, which enables the utilization of the model for program verification. The adequacy of this technique is established through proof. To validate the effectiveness of the approach, the defined memory model is integrated into CompCert, a verified C compiler, replacing its original memory model. Subsequently, two proofs of CompCert's optimization verification are updated using the simulation technique. It is anticipated that the proposed memory model will find applications in program and compiler verification tasks involving integer-pointer operations.
An Object Pseudo-Label Generation Technique based on Self-Supervised Vision Transformer for Improving Dataset Quality
Dohyun Kim, Jiwoong Jeon, Seongtaek Lim, Hongchul Lee
http://doi.org/10.5626/JOK.2024.51.1.49
Image segmentation is one of the most important tasks. It localizes objects into bounding boxes and classifies pixels in an image. The performance of an Instance segmentation model requires datasets with labels for objects of various sizes. However, the recently released "Image for Small Object Detection" dataset has large and common objects that lack labels, causing potential performance degradation. In this paper, we improve the quality of datasets by generating pseudo-labels for general objects using an unsupervised learning-based pseudo-labeling methodology to solve the aforementioned problems. Specifically, small object detection performance was improved by (+2.54 AP) compared to the original dataset. Moreover, we were able to prove an increase in performance using only a small amount of data. As a result, it was confirmed that the quality of the dataset was improved through the proposed method.
Prediction of Toothbrushing Position Based on Gyro Sensor Data and its Validation Using Unsupervised Learning-based Clustering
DoYoon Kim, MinWook Kwon, SeungJu Baek, HyeRin Yoon, DaeYeon Lim, Eunah Jo, Seungjae Ryu, Young Wook Kim, Jin Hyun Kim
http://doi.org/10.5626/JOK.2023.50.12.1143
Oral health is an important health indicator that is directly related to longevity. For this reason, oral health has become a key component of public health, from infants to the elderly. The foundation of good oral health is good brushing habits. However, the recommended correct brushing method is not easy to adopt, and this harms oral health. This paper proposes a method to distinguish brushing zones using low-cost IMU sensors to track the correct brushing method. We evaluated the accuracy of the brushing zone estimation method using clustering algorithms in machine learning. In this paper, we propose a method for determining the brushing area based on toothbrush posture alone using the gyro sensor of an IMU sensor. In this paper, we propose a method for determining the brushing area using only the gyro sensor of an IMU sensor based on toothbrush posture. We showed that relatively inexpensive 6-axis IMU gyro sensor data could be used to estimate the user’s brushing area with an accuracy of 80.6%. In addition, we applied a clustering algorithm to these data and trained a logistic regression model using the clustered data to estimate the brushing area. The result was obtained with an accuracy of 86.7%, showing that clustering was effective and that the toothbrush posture-based brushing area estimation proposed in this paper was effective. In conclusion, it is expected that the brushing zone estimation algorithm can be implemented as a function of a relatively low-cost toothbrush and that it can help to maintain oral health by analyzing and improving personal brushing habits.
Early Anomaly Detection of LNG-Carrier Main Engine System based on Multivariate Time-Series Boundary Forecasting and Confidence Evaluation Technique
Donghyun Kim, Taigon Kim, Minji An, Yunju Baek
http://doi.org/10.5626/JOK.2023.50.5.429
Recently, a variety of studies have been conducted to detect abnormal operation of ships and their causes and in the marine and shipbuilding industries. This study proposed a method for early anomaly detection of the main engine system using a multivariate time series sensor data extracted from LNG carriers built at a shipyard. For early anomaly detection, the process of predicting the future value through the sensor data at present is necessary, and in this process, the prediction residual, which is the difference between the actual future value and the predicted value, is generated. Since the generated residual has a significant effect on the early anomaly detection results, a compensating process is necessary. We propose novel loss functions that can learn the upper or lower prediction boundary of a time-series forecasting model. The time-series forecasting model trained with the proposed loss function improves the performance of the early anomaly detection algorithm by compensating the prediction residual. In addition, the real-time confidence of the predicted value is evaluated through the newly proposed confidence model by utilizing the similarity between time-series forecasting residual and confidence residual. With the early anomaly detection algorithm proposed in this study, the prediction model, which learns the upper boundary, outputs the upper limit of the predicted value that can be output by the baseline prediction model learned with the MSE loss function and can predict abnormal behavior that threshold-based anomaly discriminator could not predict because the future prediction of the baseline model is lower than the actual future value. Based on the results of this study, the performance of the proposed method was improved to 0.9532 compared to 0.4001 of the baseline model in Recall. This means that robust early anomaly detection is possible in various operating styles of the actual ship operations.
A Pre-processing Method for Learning Data Using eXplainable Artificial Intelligence
Changhong Lee, Jaemin Lee, Donghyun Kim, Jongdeok Kim
http://doi.org/10.5626/JOK.2023.50.2.133
Artificial intelligence model generation proceeds to the stages of learning data processing, model learning, and model evaluation. Data pre-processing techniques for creating quality learning data contribute many of the methods for improving model accuracy. Existing pre-processing techniques tend to rely heavily on the experience of model generators. If pre-processing is performed based on experience, it is difficult to explain the basis for selecting the corresponding pre-processing technique. However, the reason why generators are forced to rely on experience is that the learning model becomes huge and complicated to a level that is difficult for humans to interpret. Therefore, research is being conducted to explain the operation method of the model by introducing eXplainable AI. In this paper, we propose a learning data pre-processing system using eXplainable AI. The system operation process is trained with data that has not been pre-processed, the learned model is analyzed using eXplainable AI, and the data pre-processing is repeated based on that information. Finally, we will improve the model performance, explain pre-processing reliability, and show the practicality of the system.
Malware Detection Model with Skip-Connected LSTM RNN
Jangseong Bae, Changki Lee, Suno Choi, Jonghyun Kim
http://doi.org/10.5626/JOK.2018.45.12.1233
A program can be viewed as a sequence of consecutive Opcodes in which malware is a malicious program. In this paper, we assume that the program is a sequence of Opcodes with semantic information and detect the malware using the Long Short-Term Memory Recurrent Neural Network (LSTM RNN), which is a deep learning model suitable for sequence data modeling. For various experiments, the Opcode sequence is divided into a uni-gram sequence and a tri-gram sequence and used as the input features of the various deep learning models. Several deep learning models use the input Opcodes sequence to determine whether the program is a normal file or malware. We also show that the proposed Skip-Connected LSTM RNN model is superior to the LSTM encoder and the Convolutional Neural Network(CNN) model for malware detection. Experimental results show that the Skip-Connected LSTM RNN model has better performance than the LSTM encoder and CNN model in the Opcode sequence tri-gram data.
Quality Estimation of English-Korean Machine Translation using Neural Network based Predictor-Estimator Model
Hyun Kim, Jaehun Shin, Wonkee Lee, Seungwoo Cho, Jong-Hyeok Lee
http://doi.org/10.5626/JOK.2018.45.6.545
Quality Estimation (QE) for machine translation is an automatic method for estimating the quality of machine translation output without the need to use reference translations. QE has recently grown in importance in the field of machine translation (MT). Recent studies on QE have mainly focused on European languages, whereas fewer studies have been carried out on QE for Korean. In this paper, we create a new QE dataset for English to Korean translations and apply a neural network based Predictor-Estimator model to a QE task of English-Korean. Creating a QE dataset requires manual post-edited translations for MT outputs. Because Korean is a free word order language and allows various writing styles for translation, we provide guidance for creating manual post-edited Korean translations for English-Korean QE data. Also, we alleviate the imbalanced data problem of QE data. Finally, this paper reports on our experimental results of the QE task of English-Korean by using the Predictor-Estimator model trained from the created English-Korean QE data.
Link Performance Analysis of LoRa for Real-time Information Gathering in Maritime Conditions
Jaeho Shin, Junyeong Lim, Donghyun Kim, Jongdeok Kim
http://doi.org/10.5626/JOK.2018.45.3.303
LoRaWAN(Long Range Wide Area Network) is a standard for low-power, long-range, low-speed communication as announced in the LoRa Alliance. LoRaWAN addresses the physical layer and medium access control layer and the technology used in the physical layer is referred to as LoRa. LoRa can be used for remote monitoring and remote control in maritime conditions. However, unlike land, marine environment is not only difficult to construct an infrastructure for service provision, but also difficult to analyze LoRa performance in maritime. In this study, we construct an infrastructure using cloud platform and analyze LoRa link performance in maritime conditions.
Performance Analysis of LoRa(Long Range) according to the Distances in Indoor and Outdoor Spaces
Junyeong Lim, Jaemin Lee, Donghyun Kim, Jongdeok Kim
http://doi.org/10.5626/JOK.2017.44.7.733
LPWAN(Low Power Wide Area Network) technology is M2M (Machine to Machine) networking technology for the Internet of Things. The technology is designed to support low-power, long-distance and low-speed communications that are typical of LoRaWAN(Long Range Wide Area Network). To exchange inter-object information using a LoRaWAN, the link performances for various environments must be known. however, active performance analysis research that is based on an empirical environment is nonexistent. Therefore, this paper empirically evaluates the performance of the LoRa (Long Range) link, a physical communication technology of the LoRaWAN for various variables that may affect the link quality in indoor and outdoor environments. To achieve this, a physical performance monitoring system was designed and implemented. A communication experiment environment was subsequently constructed based on the indoor and outdoor conditions. The SNR(Signal to Noise Ratio), RSSI(Received Signal Strength Indication), and the PDR(Packet Delivery Ratio) were evaluated.
Risk Analysis on Various Contextual Situations and Progressive Authentication Method based on Contextual-Situation-based Risk Degree on Android Devices
Jihwan Kim, SeungHyun Kim, Soo-Hyung Kim, Younho Lee
To prevent the use of one’s smartphone by another user, the authentication checks the owner in several ways. However, whenever the owner does use his/her smartphone, this authentication requires an unnecessary action, and sometimes he/she finally decides not to use an authentication method. This can cause a fatal problem in the smartphone’s security. We propose a sustainable android platform-based authentication mode to solve this security issue and to facilitate secure authentication. In the proposed model, a smartphone identifies the current situation and then performs the authentication. In order to define the risk of the situation, we conducted a survey and analyzed the survey results by age, location, behavior, etc. Finally, a demonstration program was implemented to show the relationship between risk and security authentication methods.
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