• Atmosphere
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• v.33 no.1
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• pp.49-59
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• 2023

Blocking refers to a class of weather phenomena appearing in the mid and high latitudes, whose characteristics are blocked airflow of persistence. Frequently found over the Pacific and Atlantic regions of the Northern Hemisphere, blocking affects severe weather in the surrounding areas with different mechanisms depending on the type of blocking patterns. Along with lots of studies about persistent weather extremes focusing on the specific types of blocking, a new categorization using Rossby wave breaking has emerged. This study aims to apply this concept to the classification of blockings over the Pacific and examine how different wave breakings specify the associated cold weather in the Korean peninsula. At the same time, we investigate a strongly developing ridge around the Pacific by designing a new detection algorithm, where a reversal method is modified to distinguish ridge-type blocking patterns. As result, Kamchatka blocking (KB) and strong ridge over the Central Pacific are observed the most frequently during 20 years (2001~2020) of the studied period, and anomalous low pressures with cold air over the Korean Peninsula are accompanied by blocking events. When it considers the Rossby wave breaking, cyclonic wave-breaking is dominant in KB, which generates low-pressure anomalies over the Korean Peninsula. However, KB with anticyclone wave breaking appears with the high-pressure anomalies over the Korean Peninsula and it generates the warm temperature anomaly. Lastly, the low-pressure anomalies are also generated by the strong ridge over the Central Pacific, which persists for approximately three days and give a significant impact on cold surge on the Korean Peninsula.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.135-142
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• 2009

Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.

• Fashion & Textile Research Journal
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• v.25 no.1
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• pp.92-103
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• 2023

As the body shape change which occurs after middle age is the main factor affecting the fit of ready-to-wear clothes, this study was designed to classify and analyze the torso shapes of middle-aged men. This study sorted 3D body scans of 200 men aged 40-64 from the 8th Size Korea (2021) database and extracted their 47 measurement values using the Grasshopper algorithm for automatic extraction landmarks and measurements, developed by the previous research (Ryu & Song, 2022). Eight principal components (torso length, shoulder size, overall body size, abdomen prominence, back protrusion, neck inclination, upper body slope, and hip prominence) were identified and four torso shapes were classified. Shape 1 (28.5%) exhibited the shortest torso length, the narrowest shoulders, and the most protruding back. Shape 2 (21.0%) exhibited the skinniest body and the largest backward inclination of the upper body. Hence, the back appeared to be protruding, and the abdomen looked prominent. Shape 3 (25.5%) had the largest overall body size. Thus, the abdomen looked the least protruding, and it exhibited the flattest back. Shape 4 (25.0%) had the longest torso, widest shoulders, straightest neck, and the least protruding hips. This study suggested these three discriminant functions to identify a new person's torso type.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.95-102
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• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• The korean journal of orthodontics
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• v.53 no.3
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• pp.194-204
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• 2023

Objective: To investigate sex-specific correlations between the dimensions of permanent canines and the anterior Bolton ratio and to construct a statistical model capable of identifying the sex of an unknown subject. Methods: Odontometric data were collected from 121 plaster study models derived from Caucasian orthodontic patients aged 12-17 years at the pretreatment stage by measuring the dimensions of the permanent canines and Bolton's anterior ratio. Sixteen variables were collected for each subject: 12 dimensions of the permanent canines, sex, age, anterior Bolton ratio, and Angle's classification. Data were analyzed using inferential statistics, principal component analysis, and artificial neural network modeling. Results: Sex-specific differences were identified in all odontometric variables, and an artificial neural network model was prepared that used odontometric variables for predicting the sex of the participants with an accuracy of > 80%. This model can be applied for forensic purposes, and its accuracy can be further improved by adding data collected from new subjects or adding new variables for existing subjects. The improvement in the accuracy of the model was demonstrated by an increase in the percentage of accurate predictions from 72.0-78.1% to 77.8-85.7% after the anterior Bolton ratio and age were added. Conclusions: The described artificial neural network model combines forensic dentistry and orthodontics to improve subject recognition by expanding the initial space of odontometric variables and adding orthodontic parameters.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.210-217
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• 2022

Passive source depth estimation has been studied for decades since the source depth can be used for target classification, target tracking, etc. The purpose of this paper is to solve the problem of ambiguity in the previous paper [S.-il. Cho et al. (in Korean), J. Acoust. Soc. Kr. 38, 120-127 (2019)] that source depth is estimated in two points. The patterns of phase shift of Channel Impulse Response(CIR) reflected in ocean surface and bottom is used for removing ambiguity of the source depth estimation, and after removing ambiguity, source depth is estimated at one point through the intersection of CIR. In order to extract CIR in case of unknown source signal and continuous signal or noise, Ray-based blind deconvolution is used. The proposed algorithm is demonstrated through numerical simulation in ocean waveguide.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.360-360
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• 2022

토양수분과 유출 간 관계를 정량화하는 것은 수문 기작 및 유출 발생 과정의 이해를 위한 중요한 정보를 제공한다. 특히, 유출과정의 특성화는 수문 사상에 따른 불포화대 내 토양수 및 토사 손실 제어와 산사태 및 비점오염원 발생 예측을 위해 필수적이다. 유출과정과 관련된 비선형성과 복잡성을 확인하기 위해 토양수분과 유출 사이의 이력 거동이 조사되었다. 특히, 수문 과정 내 이력 현상 구체화를 위해 정성적인 시각적 분류 및 정량적 평가를 위한 이력 지수들이 개발되었다. 정성적인 시각적 분류는 시간에 따라 시계 및 반시계방향으로 다중 루프 형상을 나누는 방식으로 진행되었고, 정량적 평가의 경우 이력 고리(Hysteretic loop) 내 상승 고리(Rising limb)와 하강 고리(Falling limb)의 차이를 기준으로 한 지수로 이력 현상을 특성화하였다. 이전에 제안된 방법론들은 연구자의 판단이 들어가기 때문에 보편적이지 않고 이력 현상을 개발된 지수에 맞춤에 따라 자료 손실이 나타나는 한계가 존재한다. 자료의 손실 없이 불포화대 내 발생 가능한 대표 이력 현상을 자동으로 추출하기 위해 적합한 비지도 학습기반 기계학습 방법론의 제안이 필요하다. 우리 연구에서는 국내 산지 사면에서 강우 사상 동안 다중 깊이(10, 30, 60cm)로 56개의 토양수분 측정지점에서 확보된 토양수분 시계열 자료와 산지 사면 내 위어를 통해 확보된 유출 시계열 자료를 사용하였다. 먼저, 기존에 분류 방법을 기반으로 계절 및 공간특성에 따라 지배적으로 발생하는 토양수분-유출 간 이력 현상을 특성화하였다. 다음으로, 토양수분-유출 간 이력 패턴을 자료 손실 없이 형상화하여 자동으로 데이터베이스화하는 알고리즘을 개발하였다. 마지막으로, 비지도 학습방법을 이용하여 데이터베이스화된 실제 발현 이력 현상 내 확률분포를 최대한 가깝게 추정하는 은닉층을 반복적인 재구성 학습을 통해 구현함으로써 대표 이력 현상 패턴을 추출하였다.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.