Summary Among existing wireless technologies, ultra‐wideband (UWB) is the most promising solution for indoor location tracking. UWB has a great multipath fading immunity; however, great multipath resolvability alone does not eliminate the effect of non‐line‐of‐sight (NLOS) and multipath propagation. NLOS and multipath propagation in indoor environments can easily produce meters of UWB ranging error. This condition gives an enormous impact on the accuracy of indoor location tracking data. To address this problem, we propose an NLOS detection method using recursive decision tree learning. Using the UWB channel quality indicators information, we develop our model with the Gini index and altered priors splitting criteria. We then validate the constructed model using the 10‐fold cross‐validation method. Our experiment shows that the constructed model has correctly detected 90% of both line‐of‐sight (LOS) and NLOS cases on the seven different indoor environments. The result of this work can be used for the UWB indoor location tracking accuracy improvement.
Today, personal data is becoming a new economic asset. Personal data which generated from our smartphone can be used for many purposes such as identification, recommendation system, and etc. The purposes of our research are to discover human behavior based on their smartphone life log data and to build behavior model which can be used for human identification. In this research, we have collected user personal data from 37 students for 2 months which consist of 19 kinds of data sensors. There is still no ideal platform that can collects user personal data continuously and without data loss. The data which collected from user’s smartphone have various situations such as the data came from multiple sensors and multiple source information which sometimes one or more data does not available. We have developed a new approach to building human behavior model which can deal with those situations. Furthermore, we evaluate our approach and present the details in this paper.
Building energy management systems (BEMS) have been intensively used to manage the electricity consumption of residential buildings more efficiently. However, the dynamic behavior of the occupants introduces uncertainty problems that affect the performance of the BEMS. To address this uncertainty problem, the BEMS may implement load forecasting as one of the BEMS modules. Load forecasting utilizes historical load data to compute model predictions for a specific time in the future. Recently, smart meters have been introduced to collect electricity consumption data. Smart meters not only capture aggregation data, but also individual data that is more frequently close to real-time. The processing of both smart meter data types for load forecasting can enhance the performance of the BEMS when confronted with uncertainty problems. The collection of smart meter data can be processed using a batch approach for short-term load forecasting, while the real-time smart meter data can be processed for very short-term load forecasting, which adjusts the short-term load forecasting to adapt to the dynamic behavior of the occupants. This approach requires different data processing techniques for aggregation and individual of smart meter data. In this paper, we propose Lambda-based data processing architecture to process the different types of smart meter data and implement the two-level load forecasting approach, which combines short-term and very short-term load forecasting techniques on top of our proposed data processing architecture. The proposed approach is expected to enhance the BEMS to address the uncertainty problem in order to process data in less time. Our experiment showed that the proposed approaches improved the accuracy by 7% compared to a typical BEMS with only one load forecasting technique, and had the lowest computation time when processing the smart meter data.
The rapid development of RES technology produces cheaper and compact devices. This condition has attracted the household to install the RES devices on their premises. Hence, the household has changed from the passive electricity consumer into the active prosumer. The active prosumer not only consumes the electricity but also have the capability to produce electricity. However, the electricity produced by RES devices is intermittence and unstable. Moreover, the behavior of the inhabitants of the prosumer also changes over time. Hence, a smart energy management system is needed by the prosumer to maintain the balance of its electricity demand and supply. In this paper, we explore the integration of the Machine-learning based on the prosumer's EMS to address the uncertainty problem in the prosumer.
Contact Glow Discharge Electrolysis (CGDE) is one of the plasma electrolysis technologies for producing hydroxyl radicals that can be used for the process of waste degradation. This study has been conducted to establish the influence of voltage, electrolyte concentration, and anode depth in the Linear Alkylbenzene Sulfonate (LAS) degradation process using CGDE and its energy consumption. The results indicated that the greatest LAS degradation at a rate of 96.19% was achieved with an energy consumption of 2692 kJ/mmol, that was obtained using 600 V, at a Na 2 SO 4 concentration of 0.03 M, and anode depth of 20 mm during 30 minutes of the process. Meanwhile, the lowest energy consumption was 1802 kJ/mmol with the LAS degradation at the rate of 82.11% that was obtained when using 600 V, at a Na 2 SO 4 concentration of 0.03 M and anode depth of 0.5 mm during 30 minutes of the process. These results showed that CGDE is an effective method to degrade LAS.
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