• ETRI Journal
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• 제43권1호
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• pp.31-39
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• 2021

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.118-123
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• 2021

Recently, digital crime is becoming more intelligent, and efficient digital forensic techniques are required to collect evidence for this. In the case of important files related to crime, a specific person may intentionally delete the file. In such a situation, data recovery is a very important procedure that can prove criminal charges. Although there are various methods to recover deleted files, we focuses on the recovery technique using HxD editor. When recovering a deleted file using the HxD editor, check the file structure and access the file data area through calculation. However, there is a possibility that errors such as arithmetic errors may occur when a file approach through calculation is used. Therefore, in this paper, we propose an algorithm that automatically calculates the header and footer of a file after checking the file signature in the root directory for efficient file recovery. If the algorithm proposed in this paper is used, it is expected that the error rate of arithmetic errors in the file recovery process can be reduced.

• Journal of information and communication convergence engineering
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• 제19권2호
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• pp.67-72
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• 2021

Wireless sensor networks (WSNs) require an enormous number of sensor nodes (SNs) to maintain processing, sensing, and communication capabilities for monitoring targeted sensing regions. SNs are generally operated by batteries and have a significantly restricted energy consumption; therefore, it is necessary to discover optimization techniques to enhance network lifetime by saving energy. The principal focus is on reducing the energy consumption of packet sharing (transmission and receiving) and improving the network lifespan. To achieve this objective, this paper presents a novel improved energy-efficient cluster-based routing protocol (IECRP) that aims to accomplish this by decreasing the energy consumption in data forwarding and receiving using a clustering technique. Doing so, we successfully increase node energy and network lifetime. In order to confirm the improvement of our algorithm, a simulation is done using matlab, in which analysis and simulation results show that the performance of the proposed algorithm is better than that of two well-known recent benchmarks.

• 전자통신동향분석
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• 제36권2호
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• pp.22-31
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• 2021

This report introduces a definition for robot task intelligence and explains the basis of the challenges associated with implementing robot task intelligence in real-world problems. Two fundamental elements of this intelligence, robot manipulation and navigation, are introduced herein. We describe the existing trends and industrial applications of the robot task intelligence in logistics and agricultural automation. Furthermore, as an underpinning technology for this intelligence, we review the existing three-dimensional position estimation techniques and summarize the difficulties associated with applying pure SLAM technology to real-world applications. Finally, we discuss the prospects of the robot task intelligence research and its potential for solving real-world problems.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.473-477
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• 2001

Artificial neural network (ANN) is known to identify relationships even when some of the input data are very complex, ill-defined and ill-structured. One of the advantages in ANN is that it can discriminate the linearly inseparable data. This study presents an application of ANN to classify and predict the symptomatic status of HIV/AIDS patients. Even though ANN techniques have been applied to a variety of areas, this study has a substantial contribution to the HIV/AIDS care and prevention planning area. ANN model in classifying both the HIV and AIDS status of HIV/AIDS patients is developed and analyzed. The diagnostic accuracy of the ANN in classifying both the HIV status and AIDS status of HIV/AIDS status is evaluated. Several different ANN topologies are applied to AIDS Cost and Services Utilization Survey (ACSUS) datasets in order to demonstrate the model\`s capability. If ANN design models are different, it would be interesting to see what influence would have on classification of HIV/AIDS-related persons.

• 스마트미디어저널
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• 제12권3호
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• pp.112-119
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• 2023

Shrimp farming has been becoming a new source of income for fishermen in South Korea. It is often necessary for fishers to measure the size of the shrimp for the purpose to understand the growth rate of the shrimp and to determine the amount of food put into the breeding pond. Traditional methods rely on humans, which has huge time and labor costs. This paper proposes a deep learning-based method for calculating the size of shrimps automatically. Firstly, we use fine-tuning techniques to update the Mask RCNN model with our farm data, enabling it to segment shrimps and generate shrimp masks. We then use skeletonizing method and maximum inscribed circle to calculate the length and width of shrimp, respectively. Our method is simple yet effective, and most importantly, it requires a small hardware resource and is easy to deploy to shrimp farms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.754-774
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• 2023

Wrestling is one of the popular events for modern sports. It is difficult to quantitatively describe a wrestling game between athletes. And deep learning can help wrestling training by human recognition techniques. Based on the characteristics of latest wrestling competition rules and human recognition technologies, a set of wrestling competition video analysis and retrieval system is proposed. This system uses a combination of literature method, observation method, interview method and mathematical statistics to conduct statistics, analysis, research and discussion on the application of technology. Combined the system application in targeted movement technology. A deep learning-based facial recognition psychological feature analysis method for the training and competition of classical wrestling after the implementation of the new rules is proposed. The experimental results of this paper showed that the proportion of natural emotions of male and female wrestlers was about 50%, indicating that the wrestler's mentality was relatively stable before the intense physical confrontation, and the test of the system also proved the stability of the system.

• Computers and Concrete
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• 제31권2호
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• pp.139-149
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• 2023

This article investigates the statically analysis regarding the thermal buckling behavior of a nonuniform small-scale nanobeam made of functionally graded material based on classic beam theories along with the nonlocal Eringen elasticity. The material distribution of functionally graded structures is composed of temperature-dependent ceramic and metal phases in axial and thickness directions, called two-dimensional functionally graded (2D-FG). The partial differential (PD) formulations and end conditions are extracted by using to the conservation energy method. The porosity voids are assumed in the nonuniform functionally graded (FG) structure. The thermal loads are in the axial direction of the beam. The extracted nonlocal PD equations are also solved by employing generalized differential quadrature method (GDQM). Last but not least, the information acquired is used to produce miniature sensors, providing a unique perspective on the growth of nanoelectromechanical systems (NEMS).

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.124-125
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• 2023

Defect detection during liquid crystal display (LCD) manufacturing has always been a critical challenge. This study aims to address this issue by proposing a data augmentation method based on generative adversarial networks (GAN) to improve defect identification accuracy in LCD production. By leveraging synthetically generated image data from GAN, we effectively augment the original dataset to make it more representative and diverse. This data augmentation strategy enhances the model's generalization capability and robustness on real-world data. Compared to traditional data augmentation techniques, the synthetic data from GAN are more realistic, diverse and broadly distributed. Experimental results demonstrate that training models with GAN-generated data combined with the original dataset significantly improves the detection accuracy of critical defects in LCD manufacturing, compared to using the original dataset alone. This study provides an effective data augmentation approach for intelligent quality control in LCD production.

• 센서학회지
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• 제33권3호
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• pp.119-124
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• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.