地域と世界的気候様相を組み合わせた集中豪雨予測法とそのマレーシア地方への適用

九州工業大学博士学位論文（要旨） 学位記番号:情工博甲第297号 学位授与年月日:平成27年3月25

地域と世界的気候様相を組み合わせた集中豪雨予測法とそのマレーシア地方への適用

九州工業大学博士学位論文（要旨） 学位記番号:情工博甲第297号 学位授与年月日:平成27年3月25

地域と世界的気候様相を組み合わせた集中豪雨予想法とそのマレーシア地方への適応

Successive days of precipitation are known to cause flooding in monsoon-susceptible countries.The analysis of extreme precipitation trends is important for the prediction of high precipitation events. Forecasting of daily precipitation facilitates the prediction of the occurrences of rainfall and number of wet days. Using the maximum five-day accumulated precipitation (MX5d) index, we can predict the magnitude of precipitation in a specific period as it may indicate the extreme precipitation.Traditionally, a data-driven model is built on a whole data set describing the phenomenon within the data. This type of model does not considered the seasonal processes embedded in the data.Therefore, there is a need to built a model that encompassing different time scale and localized.One of the ways of doing this is to discover the different physically embedded relationships in precipitation process at different seasonal period and to built separate localized models for each of these seasonal periods. The use of committee models such as modular and ensemble models in weather and hydrological forecasting are increasing day by day. The study uses the modular concept by separating the heavy precipitation events based on sub-processes which are the seasonal monsoon and trained the subset of seasonal data using data driven models.Besides that, the study is carried out to evaluate the influence of global climate indices on local precipitation. It is interesting to see the influence of combining global and local predictors on local precipitation events. The method used past MX5d data and global climate indices such as Southern Oscillation Index (SOI), Madden Julian Oscillation (MJO), and Dipole Mode Index (DMI) in Kuantan and Kota Bharu, Malaysia using modular model trained on subset of data that represent the seasonal monsoon.The analysis started with evaluating the local and global inputs (MX5d with SOI, MJO, and DMI) in order to investigate the concurrent effect of lagged values of local precipitation data and global climate indices on seasonal extreme precipitation. Four subset of data are sampled representing two major seasonal variations in Malaysia. The experimental data are focused on the east coast area of Malaysia such that the effect of northeast monsoon season causes heavy precipitation events. The results showed that the combination of local and global modes in a modular model is favourable than a single localized mode. The proposed modular model is promising an encouraging result when different subset of data are trained on separate methods with different parameter values.九州工業大学博士学位論文 学位記番号:情工博甲第297号 学位授与年月日:平成27年3月25日1 Introduction|2 Literature Review|3 Methodology of the study|4 Data and application of methods|5 Results and discussions|6 Conclusion九州工業大学平成26年

Faktor-faktor kelewatan pembinaan projek perumahan rakyat di negeri Perak

Federal Government is currently allocating a huge budget in developing the affordable house (PPR) project under the RMK-11. However, the performance of development project in the past of RMK-10, should be relooked as it was delayed due to many reasons. Consequently, customer (owner of the project) would be affected. The aforesaid incompletion housing project may badly affect the owner of the project and will incur more cost. The Perak State Government is facing the difficulties of three (3) PPR projects, recently. Those delays project lead to the AG’s Dashboard incompliance. Thus, a study need to be done to identity the causes that may hindrance several projects, such as PPR Jalan Laksamana, Hilir Perak and overcome it effectively. This study will involve a collection of data, amongst others, content analysis, site-visit project surveillance and interview of those parties directly responsible, for instance, BPA, SUK, JKR and contractor. Qualitative method is appropriate to analyze the above mentioned data. The findings, apparently, discover several key-delay factors: project owner, project implementer and contractor. Four (4) major factors contribute to the delays: the temple and squatter relocation, the late approval of tower-crane, development order approval and the failure of contractor in evaluating the pilling works. In summary, the study will adequately furnish a necessary information and guidance in making the PPR project a success for the betterment of Perak State Government

Material Named Entity Recognition (MNER) for Knowledge-driven Materials Using Deep Learning Approach

The scientific literature contains a wealth of cutting-edge knowledge in the field of materials science, as well as useful data (e.g., numerical data from experimental results, material properties and structure). These data are critical for data-driven machine learning (ML) and deep learning (DL) methods to accelerate material discovery. Due to the large and growing number of publications, it is difficult for humans to manually retrieve and retain this knowledge. In this context, we investigate a deep neural network model based on Bi-LSTM to retrieve knowledge from published scientific articles. The proposed deep neural network-based model achieves an f-1 score of \~97\% for the Material Named Entity Recognition (MNER) task. The study addresses motivation, relevant work, methodology, hyperparameters, and overall performance evaluation. The analysis provides insight into the results of the experiment and points to future directions for current research.Comment: 10 page

Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach

The scientific literature contains an abundance of cutting-edge knowledge in the field of materials science, as well as useful data (e.g., numerical values from experimental results, properties, and structure of materials). To speed up the identification of new materials, these data are essential for data-driven machine learning (ML) and deep learning (DL) techniques. Due to the large and growing amount of publications, it is difficult for humans to manually retrieve and retain this knowledge. In this context, we investigate a deep neural network model based on Bi-LSTM to retrieve knowledge from published scientific articles. The proposed deep neural network-based model achieves an F1 score of 9 ~ 7 % for the Material Named Entity Recognition (MNER) task. The study addresses motivation, relevant work, methodology, hyperparameters, and overall performance evaluation. The analysis provides insight into the results of the experiment and points to future directions for current research

Umpcare counselling system using ionic angular

Counselling Unit is an organization that can provide support to the problematic student. They will guide these students to identify their problem and find a way to solve them. They have provided several types of service for the student such as deliver counselling session and career advice. P1 and P2 student is required to get consultation from the counsello

Dengue dasboard for forecasting the future trend of dengue cases in Pahang using auto regression integrated moving average (ARIMA) model

The case study of this project is focuses on developing a dengue dashboard for forecasting the future trend of dengue cases in Pahang from 2021 to 2023 using ARIMA model. This dashboard also includes the latest information regarding the total number of dengue cases in Pahang, total deaths caused by dengue in Pahang, total cases according to each district in Pahang, interactive map of dengue cases in Pahang and a graph displaying the future trend of dengue cases

REDf: A Renewable Energy Demand Forecasting Model for Smart Grids using Long Short Term Memory Network

The integration of renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity. In this paper, we propose a deep learning-based approach for predicting energy demand in a smart power grid, which can improve the integration of renewable energy sources by providing accurate predictions of energy demand. We use long short-term memory networks, which are well-suited for time series data, to capture complex patterns and dependencies in energy demand data. The proposed approach is evaluated using four datasets of historical energy demand data from different energy distribution companies including American Electric Power, Commonwealth Edison, Dayton Power and Light, and Pennsylvania-New Jersey-Maryland Interconnection. The proposed model is also compared with two other state of the art forecasting algorithms namely, Facebook Prophet and Support Vector Regressor. The experimental results show that the proposed REDf model can accurately predict energy demand with a mean absolute error of 1.4%. This approach has the potential to improve the efficiency and stability of the power grid by allowing for better management of the integration of renewable energy sources

Suspicious activity trigger system using YOLOv6 convolutional neural network

Property theft is one of the crimes that increases in which leads to a major concern in Malaysia. Despite of having surveillance cameras (CCTV) everywhere, the crimes keep occur due to the lack of security system. The security system can be developed by utilizing the existence of CCTVs specifically home surveillance CCTV. Therefore, this paper introduces a security system known as Suspicious Activity Trigger System (SATS) that able to automatically trigger an alarm or an alert message whenever suspicious activity is detected from the CCTV video image. The activity will be detected in a video image using Deep Learning technique which is YOLOv6 Convolutional Neural Network (CNN) algorithm. The algorithm will detect an object which is a person in the video and classify it as a suspicious activity or not. If the activity is classified as the suspicious activity, the system will automatically display a trigger message to alert SATS user. The user can therefore take whatever appropriate measure to prevent being a victim. Experiments have been conducted using a dataset taken from Google Open Image. We also implemented the experiments on the self-obtained dataset. Based on the experiment, 92.53% for precision and 96.6% of the accuracy is obtained using this algorithm. Therefore, YOLOv6 can be implemented in the security system to prevent crimes in residency areas