• Smart Media Journal
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• v.11 no.9
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• pp.47-55
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• 2022

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.9-15
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• 2010

Recently, Smart Grid is a emerging topic in power and communication industry. Smart Grid refers to a evolution of the electricity supply infrastructure that monitors, protects, and intelligently optimize the operation of the interconnected elements including various type of generators, power grid, building/home automation system and end-use consumers. In order to successful implementation of Smart Grid, energy management function will be the key factor that coordinates and optimally controls the various loads according to the operating condition and environments, and the load patterns in residential and commercial building will be required as fundamental element for load management. In this study, we collects many types of energy usage data of electric appliances, analyze their load curves, and make the general load patterns for electrical appliance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.9
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• pp.90-102
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• 2006

In this study, it has been attempted to build up a smart, stable, and integrated Next-generation Home Network (NHN) system capable of supporting more comfortable and convenient life in the future human housing domains, and to find out which institutional tasks are required to activate the related industries. Additionally, this study aims to review a variety of technologies suggested so far in several fields including information and communications, appliance, and building maintenance. This paper analyzes the advantages and disadvantages, acceptance structure and problems of standard technologies and it suggests the political methods and policies to embody them most effectively and integrally in the future. The said certification system is called 'Standardized Certification System for Korean-style Next-generation Home Network Buildings'(abbreviated to K-NHN) as a new system created for the purpose of developing, more effectively, NHN to be introduced in the future.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.444-448
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• 2008

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2:image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• Annual Conference of KIPS
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• 2017.11a
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• pp.1137-1140
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• 2017

본 연구에서는 Java기반 Android App과 라즈베리파이 보드를 기반으로 Wi-fi 통신을 통해 스마트폰과 라즈베리파이를 연동하여 집안의 커튼, 전구, 카메라 등과 같은 기기들을 제어하는 시스템을 구현하는 방법을 제시하였다. 기존의 가정의 전구와 커튼과 같은 전자기기들을 스마트폰을 통하여 자택에서 편안하게 제어할 수 있으며 향후 지어지는 신축 아파트들이나 병원과 같은 시설에서 이러한 IoT 기술이 접목될 가능성이 있기 때문에 라즈베리파이와 하드웨어를 연동하여 가정기기를 제어하는 시스템을 구현하였다.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.572-580
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• 2013

Power metering and monitoring system is a basic element of Smart Grid technology. This paper proposes a new Non-Intrusive Load Monitoring (NILM) method for a residential buildings sector using the measured total active power consumption. Home electrical appliances are classified by ON/OFF state models, Multi-state models, and Composite models according to their operational characteristics observed by experiments. In order to disaggregate the operation and the power consumption of each model, an algorithm which includes a switching function, a truth table matrix, and a matching process is presented. Typical profiles of each appliances and disaggregation results are shown and classified. To improve the accuracy, a Time Lagging (TL) algorithm and a Permanent-On model (PO) algorithm are additionally proposed. The method is validated as comparing the simulation results to the experimental ones with high accuracy.

• 한국IT서비스학회:학술대회논문집
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• 2007.05a
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• pp.488-493
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• 2007

최근 정보통신 기술의 발전으로 인하여 어느 장소에서든 컴퓨터 또는 무선장치를 사용하여 집안의 지능형 가전기기들을 점검하고 제어 할 수 있는 수준까지 이르렀다. 이와 같은 서비스를 제공하기 위한 요소기술 중에 마이크로소프트사에서 발표한 UPnP(Universal Plug & Play)는 TCP/IP 프로토콜을 기반으로 하여 각 지능형 가전기기에 IP주소를 할당하여 어느 곳에서나 누구나 편리하게 지능형 가전기기의 서비스를 이용할 수 있도록 해준다. 본 논문에서는 UPnP 미들웨어 기반의 홈 네트워크에서 웹 기반의 인터페이스를 통해 지능형 가전기기를 쉽게 관리할 수 있는 홈 게이트웨이와 지능형 가전기기 간 연동 시스템을 설계하고 구현한다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.155-162
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• 2007

For providing smart home service, middleware technologies for electronic appliance control by network and user location information for location based service are important. Recently research using ultrasonic and radio signal are affected by the obstacle. In this paper, we suggest inertial sensor that is not affected by the obstacle. Also, we use RFID for initializing position. It solve error accumulation and position initialize problem. In this paper, we suggest following system for smarthome service and localization. This system are composed smarthome middleware, user localization system on middleware, inertial sensor and RFID Reader. This system shows operation without affect of obstacle in smarthome environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1223-1230
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• 2021

Recently, energy resource management is a major concern around the world. Energy management activities minimize environmental impacts of the energy production. This paper presents design and prototyping of a home electric energy monitoring system that provides residential consumers with real time information about their electricity use. The developed system is composed of an in-house sensing system and a server system. The in-home sensing system is a set of wireless smart plug which have an AC power socket, a relay to switch the socket ON/OFF, a CT sensor to sense current of load appliance and a Kmote. The Kmote is a wireless communication interface based on TinyOS. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to residential consumers. We analyzed and presented energy consumption data from electrical appliances for 3 months in home. The experimental results show the promising possibilities to estimate the energy consumption patterns and the current status.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.21-28
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• 2021

In this study, we propose a deep learning-based NILM technique using actual measured power data for 5 kinds of home appliances and verify its effectiveness. For about 3 weeks, the active power of the central power measuring device and five kinds of home appliances (refrigerator, induction, TV, washing machine, air cleaner) was individually measured. The preprocessing method of the measured data was introduced, and characteristics of each household appliance were analyzed through spectogram analysis. The characteristics of each household appliance are organized into a learning data set. All the power data measured by the central power measuring device and 5 kinds of home appliances were time-series mapping, and training was performed using a LSTM neural network, which is excellent for time series data prediction. An algorithm that can disaggregate five types of energies using only the power data of the main central power measuring device is proposed.