• Journal of IKEEE
• /
• v.24 no.2
• /
• pp.632-637
• /
• 2020

The non-intrusive load monitoring (NILM) is an important way to cost-effective real-time monitoring the energy consumption and time of use for each appliance in a home or business using aggregated energy from a single recording meter. In this paper, we collect from the smart farm's power consumption data acquisition system to the server via an LTE modem, converted the total power consumption, and the power of individual electric devices into HDF5 format and performed NILM analysis. We perform NILM analysis using open source denoising autoencoder (DAE), long short-term memory (LSTM), gated recurrent unit (GRU), and sequence-to-point (seq2point) learning methods.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.4
• /
• pp.1519-1523
• /
• 2015

Infection of the uterine cervix by human papilloma viruses (HPV) may be associated with cervical pre-cancer and invasive cervical carcinoma if left untreated. With advance in molecular techniques, it has become easier to detect the resence of HPV DNA long before the appearance of any lesion. This study concerned cervical scrape samples of 310 married non-pregnant women attending a gynecology outpatient department for both Pap and PCR testing to detect HPV DNA. Nested PCR using primers for L1 consensus gene with My9/My11 and GP6+/GP5+followed by multiplex PCR were carried out to detect HPV 16 and HPV18. Result: HPV prevalence was 11.9% out of which 3.67% cases of negative for intra-epithelial lesion or malignancy (NILM) and in 71.1% (27/38) of atypical cervical smears were HPV positive. There was increasing trend of high-risk-HPV positivity (HR HPV 16 and 18), from 20% in benign cytology (NILM) to 42.9 % in LSIL, 71.41% in HSIL and 100% in SCC. There was highly significant association of HPV infection with cervical lesion ($x^2=144.0$, p<0.01) and also with type specific HPV prevalence ($x^2=7.761^*$, p<0.05).

• Journal of IKEEE
• /
• v.23 no.1
• /
• pp.322-325
• /
• 2019

The non-intrusive load monitoring(NILM) algorithm can infer the power usage of the individual electric devices by the total electric power consumption of the main line. To develop such an algorithm, power usage pattern data of individual devices as well as those of various combinations of these devices are required. In this paper, we propose a method to develop a power usage pattern data acquisition system for developing a NILM algorithm for a smart farm. The data acquisition system is capable of simultaneously measuring the power usage of individual electrical devices and the power usage according to various combinations of scenarios every second. The measured data can be remotely monitored from the outside of the smart farm through the LTE network, and the measured data is stored in an external server.

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

• IEMEK Journal of Embedded Systems and Applications
• /
• v.10 no.4
• /
• pp.213-219
• /
• 2015

Real-time energy monitoring systems is a demand-response system which is reported to be effective in saving energy up to 12%. Real-time energy monitoring system is commonly composed of smart-plugs which sense how much electrical power is consumed and IHD(In-Home Display device) which displays power consumption patterns. Even though the monitoring system is effective, users should themselves match which smart plus is connected to which appliance. In order to make the matching work to be automatic, the monitoring system need to have appliance identification algorithm, and some works have made under the name of NILM(Non-Intrusive Load Monitoring). This paper proposed an algorithm which utilizes multiple classifiers to improve accuracy of appliance identification. The algorithm proposes to understand each classifiers performance, that is, when a classifier make a result how much the result is reliable, and utilize it in choosing the final result among result candidates from many classifiers. By using the proposed algorithm this paper make 4.5% of improved accuracy with respect to using single best classifier, and 2.9% of improved accuracy with respect to other method using multiple classifiers, so called CDM(Commitee Decision Mechanism) method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.6
• /
• pp.679-685
• /
• 2011

This paper presents an algorithm identifying devices that generate observed mixed signals that are collected at main power-supply line. The proposed algorithm, which is necessary for low cost electric power monitoring system at appliance-level, that is non-intrusive load monitoring system, divides incoming mixed signal into multiple time intervals, calculating difference-signals between consecutive time interval, and identifies which device is operating at the time interval by analysing the difference-signals. Since the features of one device can remain when the time interval is short enough and the features are independent and additive, well-known classification algorithms can be used to classify the difference-signals with features of N individual devices, otherwise $2^N$ features might be necessary. The proposed algorithm was verified using data mixed in a laboratory with individual devices's data collected from field. When maximum 4 devices operate or stop sequentially and when features satisfy the requirements of proposed algorithm, the proposed algorithm resulted nearly 100% success rate under the constrained test condition. In order to apply the proposed algorithm in real world, the number devices shall increase, the time interval shall be smaller and the pattern of mixture shall be more diverse. However we can expect, if features used follow guidelines of proposed algorithm, future system could have certain level of performance without the guideline.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.4
• /
• pp.109-116
• /
• 2021

In this paper, we propose a non-intrusive load monitoring(NILM) system which can find the power of each home appliance from the aggregated total power as the activation in the trading market of the distributed resource and the increasing importance of energy management. We transform the amount of appliances' power into a power on-off state by preprocessing. We use LSTM as a model for predicting states based on these data. Accuracy is measured by comparing predicted states with real ones after postprocessing. In this paper, the accuracy is measured with the different number of electronic products, data postprocessing method, and Time step size. When the number of electronic products is 6, the data postprocessing method using the Round function is used, and Time step size is set to 6, the maximum accuracy can be obtained.

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

• Proceedings of the KIEE Conference
• /
• 2015.07a
• /
• pp.45-46
• /
• 2015

가전기기별 에너지 사용정보를 제공함으로써 가정에서 효율적인 에너지 사용을 유도할 수 있다. 가전기기별 사용정보를 효과적으로 제공하기 위해서는 NILM (Non-Intrusive Load Monitoring) 기법이 필요하다.본 논문에서는 개별 가전기기 분류단계에서 쓰이는 DTW(Dynamic Time Warping) 기법을 소개한다. DTW 기법은 다른 두 시계열 데이턴간의 유사도를 측정하는 패턴인식 기법 중 하나이다. 이 유사도를 이용하여 가전기기의 동작여부를 판별하고 분류를 수행한다.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.10
• /
• pp.4665-4669
• /
• 2016

Purpose: To evaluate the legitimacy of a diagnosis of ASC-H in 5 cases which were followed up monthly for over 2 years with both cytology and HPV testing. Methods: Some 5 cases out of a total of 25.0 self-sampled Pap test patients diagnosed as ASC-H provided 119 specimens over 2 years, with HPV-DNA testing perormed using a E6 primer. Results: Cases 1, 2 and 3 showed SIL after the ASC-H diagnosis, while cases 4 and 5 showed and maintained NILM. Cases 1, 2 and 3 were further characterized by small atypical compressed binucleated cells, in which HPV was detected by in situ PCR. Case 4 showed a high N/C ratio in cells in sheets with a mild increase in chromatin. Case 5 demonstrated a high N/C ratio in small cells with no increase in chromatin. Conclusion: The finding of a compressed binucleated cells can define the difference between degenerated endocervical columnar cells and small atypical cells suggestive of HSIL. When small compressed binucleated squamous cells are detected, there may be a chance of continuing HPV infection and undetected SIL.