• 대한임베디드공학회논문지
• /
• 제18권6호
• /
• pp.319-325
• /
• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• International Journal of Contents
• /
• 제5권3호
• /
• pp.50-56
• /
• 2009

Internet is being used for several activities by a great range of users. These activities include communication, e-commerce, education, and entertainment. Users are required to register regarding website in order to enroll web activities. However, registration can be done by automated hacking software. That software make false enrollments which occupy the resources of the website by reducing the performance and efficiency of servers, even stop the entire web service. It is crucial for the websites to have a system which has the capability of differing human users and computer programs in reading images of text. Completely Automated Public Turing Test to Tell Computers and Human Apart (CAPTCHA) is such a defense system against Optical Character Recognition (OCR) software. OCR can be defined as software which work for defeating CAPTCHA images and make countless number of registrations on the websites. This study proposes a new CAPTCHA context that is Korean CAPTCHA by means of the method which is splitting CAPTCHA images into several parts with random rotation values, and drawing random lines on a grid background by using Korean characters only. Lines are in the same color with the CAPTCHA text and they provide a distortion of image with grid background. Experimental results show that Korean CAPTCHA is a more secure and effective CAPTCHA type for Korean users rather than current CAPTCHA types due to the structure of Korean letters and the algorithm we are using: rotation and splitting. In this paper, the algorithm of our method is introduced in detail.

• 한국멀티미디어학회논문지
• /
• 제15권8호
• /
• pp.1059-1066
• /
• 2012

오늘날 스마트폰의 발전으로 스마트폰 내장 센서를 통해 사용자의 개인 정보를 쉽게 파악 할 수 있고 원한다면 사용자의 위치를 실시간으로 알아낼 수 있다. 그리하여 센서를 통해 추출된 데이터를 통해 동작인식과 생활 패턴 인식에 관한 연구가 급증하고 있다. 본 논문에서는 기존의 동작 인식 연구에서 추출되는 데이터를 정형화하기 위해 동작 데이터를 모델링하였다. 본 논문의 일상 동작 모델링은 이론적 분석이다. 동작을 크게 두 가지로 분류시켜 가속도 센서만으로 인식 가능한 기본 동작을 물리적 동작으로 정의하고 그 외 목적과 대상, 장소를 포함하는 모든 동작을 논리적 동작으로 분류시켰다. 모델링 된 데이터를 기반으로 각 동작의 특성에 맞게 가시화 하는 방안을 제안하였다. 본 연구를 통해 인간의 일상생활을 동작별로 간편하게 표준화 할 수 있고 기존의 동작 인식 연구에서 추출되는 동작 데이터를 사용자의 요구에 따라 가시화 할 수 있다.

• 아동학회지
• /
• 제28권3호
• /
• pp.163-173
• /
• 2007

This study analyzed ecological elements of 140 nursery rhymes, stories, and plays in the $6^{th}$ edition of the Guide for Educational Activities and Materials for Kindergarten. The results show that 24 works(17.1%) have ecological elements. Content analysis of ecological elements in these works show works that deal with the side effects of environmental development(3 nursery stories), the cult of nature(1 nursery rhyme), recognition of the eco-cycle(2 nursery rhymes, 4 stories, 2 plays), dignity of life and recovery of human nature(12 nursery stories). On the basis of the results, children's literature containing ecological elements were suggested.

• Communications for Statistical Applications and Methods
• /
• 제30권6호
• /
• pp.551-560
• /
• 2023

Recently, there has been significant research into the recognition of human activities using three-dimensional sequential skeleton data captured by the Kinect depth sensor. Many of these studies employ deep learning models. This study introduces a novel feature selection method for this data and analyzes it using machine learning models. Due to the high-dimensional nature of the original Kinect data, effective feature extraction methods are required to address the classification challenge. In this research, we propose using the first four moments as predictors to represent the distribution of joint sequences and evaluate their effectiveness using two datasets: The exergame dataset, consisting of three activities, and the MSR daily activity dataset, composed of ten activities. The results show that the accuracy of our approach outperforms existing methods on average across different classifiers.

• 대한인간공학회지
• /
• 제31권4호
• /
• pp.593-599
• /
• 2012

Objective: This study aims to implement a non-contact gesture-based interface for presentation purposes and to analyze the effect of the proposed interface as information transfer assisted device. Background: Recently, research on control device using gesture recognition or speech recognition is being conducted with rapid technological growth in UI/UX area and appearance of smart service products which requires a new human-machine interface. However, few quantitative researches on practical effects of the new interface type have been done relatively, while activities on system implementation are very popular. Method: The system presented in this study is implemented with KINECT$^{(R)}$ sensor offered by Microsoft Corporation. To investigate whether the proposed system is effective as a presentation support tool or not, we conduct experiments by giving several lectures to 40 participants in both a traditional lecture room(keyboard-based presentation control) and a non-contact gesture-based lecture room(KINECT-based presentation control), evaluating their interests and immersion based on contents of the lecture and lecturing methods, and analyzing their understanding about contents of the lecture. Result: We check that whether the gesture-based presentation system can play effective role as presentation supporting tools or not depending on the level of difficulty of contents using ANOVA. Conclusion: We check that a non-contact gesture-based interface is a meaningful tool as a sportive device when delivering easy and simple information. However, the effect can vary with the contents and the level of difficulty of information provided. Application: The results presented in this paper might help to design a new human-machine(computer) interface for communication support tools.

• 한국환경독성학회:학술대회논문집
• /
• 한국환경독성학회 2004년도 Sharing Experience of Human and Ecological Risk Assessment and Management in Asia/Pacific Region
• /
• pp.78-80
• /
• 2004

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
• /
• 제15권1호
• /
• pp.72-76
• /
• 2009

최근 유비쿼터스 컴퓨팅에 대한 관심이 높아지면서 유비쿼터스 환경에서의 서비스를 위한 인간과 컴퓨터의 상호 작용, 특히 인간의 행동을 인식하는 연구가 활발히 진행되고 있다. 기존의 영상기반 연구와는 달리 모바일 환경에 적합하도록 가속도 센서, 생리신호 센서 등 다양한 센서들을 활용하여 사용자의 행동을 인식하는 기법이 연구되고 있다. 본 논문에서는 멀티모달 센서들을 통합하고 동적 베이지안 네트워크를 계층적으로 구성하여 사용자의 행동을 인식하는 방법을 제안한다. 연산량이 비교적 적은 베이지안 네트워크로 전반적인 사용자 행동을 추론하고 획득된 각 행동의 확률순으로 동적 베이지안 네트워크를 구성한다. 동적 베이지안 네트워크는 OVR(One-Versus-Rest) 전략으로 학습되며, 확률순으로 행동이 검증되어 임계치를 넘는 경우 선택된 행동보다 낮은 확률의 행동에 대한 동적 베이지안 네트워크를 검증하지 않아 추론 연산량을 줄인다. 본 논문에서는 가속도 센서와 생리적 신호 센서를 기반으로 총 8가지의 행동을 인식하는 문제에 제안하는 방법을 적용하여 평균적으로 97.4%의 분류 정확률을 얻었다.

• 대한인간공학회지
• /
• 제17권3호
• /
• pp.23-39
• /
• 1998

Several recent aircraft accidents occurred due to goal conflicts between human and machine actors. To facilitate the management of the cockpit activities considering these observations. a computational aid. the Agenda Manager (AM) has been developed for use in simulated cockpit environments. It is important to know pilot intentions performing cockpit operations accurately to improve AM performance. Without accurate knowledge of pilot goals or intentions, the information from AM may lead to the wrong direction to the pilot who is using the information. To provide a reliable flight simulation environment regarding goal conflicts. a pilot goal communication method (GCM) was developed to facilitate accurate recognition of pilot goals. Embedded within AM, the GCM was used to recognize pilot goals and to declare them to the AM. Two approaches to the recognition of pilots goals were considered: (1) The use of an Automatic Speech Recognition (ASR) system to recognize overtly or explicitly declared pilot goals. and (2) inference of covertly or implicitly declared pilot goals via the use of an intent inferencing mechanism. The integrated mode of these two methods could overcome the covert goal mis-understanding by use of overt GCM. And also could it overcome workload concern with overt mode by the use of covert GCM. Through simulated flight environment experimentation with real pilot subjects, the proposed GCM has demonstrated its capability to recognize pilot intentions with a certain degree of accuracy and to handle incorrectly declared goals. and was validated in terms of subjective workload and pilot flight control performance. The GCM communicating pilot goals were implemented within the AM to provide a rich environment for the study of human-machine interactions in the supervisory control of complex dynamic systems.

• 동아시아식생활학회지
• /
• 제25권6호
• /
• pp.952-962
• /
• 2015

The purpose of this study was to investigate the effect of green dietary life recognition and low-carbon green life practice on health-related dietary habits in high school students. The subjects were 367 high school students in the Incheon area. This cross-sectional survey was conducted using a questionnaire, and data were analyzed with the SPSS 20.0 program. According to the findings, green dietary life recognition were categorized into two sub-factors: 'Eco-friendly traditional dietary life', and 'Life of consideration and thanks'. Low-carbon green life practice was 'Low-carbon green life', and health-related dietary habits were categorized into four sub-factors: 'Vegetables-oriented traditional dietary habits', 'Balanced dietary habits', 'Life practice for health', and 'Various cereals intake'. Green dietary life recognition showed a significantly positive relationship with all sub-factors of health-related dietary habits (p<0.05), whereas 'Eco-friendly traditional dietary life' had no significant effect on 'Balanced dietary habits'. Low-carbon green life practice showed a significantly positive relationship with all sub-factors of health-related dietary habits (p<0.01). Students who received green growth education showed significantly higher health-related dietary habits than those who did not (p<0.01). Girls showed significantly higher green dietary life recognitions and low-carbon green life practice than boys (p<0.01). Therefore, more green dietary life and low-carbon green life education programs targeting students are need. Voluntary activities, along with green dietary life and low-carbon green life education will help students improve their health-related dietary habits.