• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.8
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• pp.29-35
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• 2010

In this paper we propose a new pattern generator for a BIST architecture that can reduce the power consumption during test application. The principle of the proposed method is to reconstruct an LFSR circuit to reduce WSAs of the heavy nodes by suppressing the heavy inputs. We propose algorithms for finding heavy nodes and heavy inputs. Using the Modified LFSR which consists of some AND/OR gates trees and an original LFSR, BIST applies modified test patterns to the circuit under test. The proposed BIST architecture with small hardware overhead effectively reduces the average power consumption during test application while achieving high fault coverage. Experimental results on the ISCAS benchmark circuits show that average power reduction can be achieved up to 30.5%.

• The Journal of Bigdata
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• v.6 no.1
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• pp.127-132
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• 2021

Korea is both a resource-poor country and a energy-consuming country. In addition, the use and dependence on electricity is very high, and more than 20% of total energy use is consumed in buildings. As research on deep learning and machine learning is active, research is underway to apply various algorithms to energy efficiency fields, and the introduction of building energy management systems (BEMS) for efficient energy management is increasing. In this paper, we constructed a database based on energy usage by device per household directly collected using smart plugs. We also implement algorithms that effectively analyze and predict the data collected using RNN and LSTM models. In the future, this data can be applied to analysis of power consumption patterns beyond prediction of energy consumption. This can help improve energy efficiency and is expected to help manage effective power usage through prediction of future data.

• Journal of Korean Society of Transportation
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• v.34 no.3
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• pp.234-247
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• 2016

As the necessity of the evaluation of environmentally-friendly traffic operations strategies becomes obvious, the characteristics of fuel consumption models should be comprehended in advance. This study selected three fuel consumption models developed in Korea and another three models widely used in North America, and compared their applicabilities. Specifically, the national institute of environmental research (NIER) drive modes and the VISSIM software were utilized to model various driving patterns, and their fuel consumptions were estimated using the fuel consumption models. Based on the results, all the models showed the similar results in the analysis of the most fuel efficient cruising speed. On the other hand, caution should be taken when using the KR-1 and KR-2 models in microscopic analyses because they are not sensitive to instantaneous power requirements of vehicles.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.117-123
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• 2013

By exploiting the regular read and write access patterns of embedded SRAM memories inside Viterbi decoder, the memory architecture can be efficiently modified to reduce the power consumption of write operation. According to the experimental results with 65nm CMOS process, the proposed embedded memory used for Viterbi decoder achieves 30.84% of power savings with 8.92% of area overhead compared to the conventional embedded SRAM approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.65-70
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• 2021

Recently, as the number of mobile apps rapidly increases, the memory size of smartphones keeps increasing. Smartphone memory consists of DRAM and as it is a volatile medium, continuous refresh operations for all cells should be performed to maintain the contents. Thus, the power consumption of memory increases in proportion to the DRAM size of the system. There are attempts to configure the memory system with low-power non-volatile memory instead of DRAM to reduce the power consumption of smartphones. However, non-volatile memory has weaknesses in write operations, so analysis of write behaviors is a prerequisite to realize this in practical systems. In this paper, we extract memory reference traces of mobile apps and analyze their characteristics specially focusing on write operations. The results of this paper will be helpful in the design of memory management systems consisting of non-volatile memory in future smartphones.

• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1158-1164
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• 2020

In Korea, the dependence on imported energy is very high, and research to reduce standby power is being conducted based on Zigbee, a low-power technology, to reduce wasted power and improve power efficiency. However, because Zigbee is not an IoT standard protocol and is not network-based, it is necessary to build a network with a separate gateway, and research on standby power is insufficient because the standards for international power consumption of devices are ambiguous. Therefore, in this paper, we applied the IoT standard protocol MQTT to the existing Zigbee technology to build a network network without a separate gateway, and designed and implemented a standby power reduction system that collects standby power degradation and user patterns. As a result of evaluating with the existing system, it was confirmed that about 7.11% of standby power was consumed compared to the existing system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.74-78
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• 2015

With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.777-780
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• 2014

The sensor network technology for core technology of ubiquitous computing is in the spotlight recently, the research on sensor network is proceeding actively which is composed many different sensor node. The major traffic patterns of plenty of sensor networks are composed of collecting types of single directional data, which is transmitting packets from several sensor nodes to sink node. One of the important condition for design of sensor node is to extend for network life which is to minimize power-consumption under the limited resources of sensor network. In this work, we analysis adapted routing protocols using the network simulation that was used exiting network and network provider needs will be able to solve the problem.