Assessment of renewable distributed generation in green building rating system for public hospital

This paper presents an optimization solution for renewable Distributed Generation (DG), as imposed in the Green Building Rating System (GBRS) for a public hospital. Solar photovoltaic DG unit (PV-DG) is identified as a type of DG used in this paper. The proposed optimization via PV-DG coordination will improve the sustainable energy performance of the green building by power loss reduction within accepted lower losses region using Artificial Bee Colony (ABC) algorithm. The setup input data from one of Malaysian public hospitals’ power distribution system is been adopted and simulation results via MATLAB programming show that the optimization of DG forming into bigger-scale imposed system provides a better outcome in minimization of total power losses within appropriate voltage profile as compared to current PV-DG imposed in GBRS. The objective function representing total power losses which also supported by related literature give a measure that forming sufficient and optimal PV-DG assessment criteria is highly important, thus, current PV-DG assessment in GBRS is proposed to be reviewed into new parameter setting for public hospital due to its’ high energy demand and distinctive electrical load profile

Distribution Network Loss Minimization via Artificial Immune Bee Colony

芝浦工業大学201

A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis

Handgrip strength (HGS) is an easy-to-use instrument for monitoring people's health status. Numerous researchers in many countries have done a study on handgrip disease or demographic data. This study focused on classifying aged groups referring to handgrip value using machine learning. A total of fifty-four participants had involved in this study, ages ranging from 24 years to 57 years old. Digital Pinch Grip Analyzer had been used to measure the handgrip measurement three times to get more accurate results. The result is then recorded by Clinical Analysis Software (CAS) that is built into the analyzer. An independent t-test is used to investigate the significant factor for age group classification. The data were then classified using machine learning analysis which are Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes. The overall dataset shows that the Support Vector Machine is the most suitable classification technique with average accuracy between 5 groups of age is 98%, specificity of 0.79, the sensitivity of 0.9814 and 0.0185 of mean absolute error. SVM also give the lowest mean absolute error compared to RF and Naïve Bayes. This study is consistent with the previous work that there is a relationship between handgrip and age

A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis

Handgrip strength (HGS) is an easy-to-use instrument for monitoring people's health status. Numerous researchers in many countries have done a study on handgrip disease or demographic data. This study focused on classifying aged groups referring to handgrip value using machine learning. A total of fifty-four participants had involved in this study, ages ranging from 24 years to 57 years old. Digital Pinch Grip Analyzer had been used to measure the handgrip measurement three times to get more accurate results. The result is then recorded by Clinical Analysis Software (CAS) that is built into the analyzer. An independent t-test is used to investigate the significant factor for age group classification. The data were then classified using machine learning analysis which are Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes. The overall dataset shows that the Support Vector Machine is the most suitable classification technique with average accuracy between 5 groups of age is 98%, specificity of 0.79, the sensitivity of 0.9814 and 0.0185 of mean absolute error. SVM also give the lowest mean absolute error compared to RF and Naïve Bayes. This study is consistent with the previous work that there is a relationship between handgrip and age

Statistical feature analysis of EEG signals for calmness index establishment

Electroencephalographic (EEG) signals are very closely related to psychophysiological. The EEG signals displayed few responses which can be categorized. This article discussed the use of statistics over the EEG features which confirm the different mental characteristics. Two different type of stimulus was given named as relaxed state and non-relaxed state. Asymmetry index was computed as the EEG features via the alpha waves and was extracted during the relaxed state and the non-relaxed state. The EEG features were clustered to a group of three, four and five using Fuzzy C-Means. During the relaxed state, the alpha wave showed a higher response as compared to the non-relaxed state. This is observed by using the mean relative energy between the relaxed state and non-relaxed state. To ensure which EEG features in the clusters showed a significant difference, p < .05, a statistical test was used. Wilcoxon Signed Ranks test is the best-statistical test to verify the selected clusters as it is suitable to analyze the small sample of data. Wilcoxon Signed Ranks test used a hypothesis testing which using the same method as paired sample t-test. The advantage in using Wilcoxon Signed Ranks test is that, it uses the median to get the difference between two samples of data. Analytical results showed that the data features of four clusters and three clusters give a significant difference, thus the obtained results can be used to further up the study. The Wilcoxon Signed Ranks test results confirmed that the proposed technique has potential in establishing the calmness index

Optimal capacitor placement and sizing via artificial bee colony

Abstract To achieve a more economical distribution system in the future, several methods have been introduced by researchers to accomplish that goal. Among the most commonly used method is to install the capacitors. It operates by supplying reactive power into the system to improve the performance of voltage, thereby reducing power losses. Nevertheless, the location and the size of the capacitor still issues to be resolved by the utilities. Various methods have been introduced to coordinate the capacitor without affect the performance of the distribution system. Basically, the most popular approach used to determine the location of capacitors is based on sensitivity analysis. This approach operates by placing the capacitor at each node in the system and selects the node that gives higher power losses reduction. Meanwhile, the size of capacitor is determined by using the optimization techniques in obtaining optimal values. However, calculation for both location and size in separate analysis could lead the solution trapped in local optimum. Therefore, this paper is investigated a solution to determine the location and size of capacitor simultaneously by using artificial bee colony (ABC). The effectiveness of proposed method is tested on 33-bus and 69-bus test system and compared with other methods. Based from the obtained results, simultaneous approach reduces the power losses by 34.29% and 35.44% for 33-bus and 69-bus test system, respectively. Moreover, the proposed method gives a better voltage improvement compared to the base case

A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis

Handgrip strength (HGS) is an easy-to-use instrument for monitoring people's health status. Numerous researchers in many countries have done a study on handgrip disease or demographic data. This study focused on classifying aged groups referring to handgrip value using machine learning. A total of fifty-four participants had involved in this study, ages ranging from 24 years to 57 years old. Digital Pinch Grip Analyzer had been used to measure the handgrip measurement three times to get more accurate results. The result is then recorded by Clinical Analysis Software (CAS) that is built into the analyzer. An independent t-test is used to investigate the significant factor for age group classification. The data were then classified using machine learning analysis which are Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes. The overall dataset shows that the Support Vector Machine is the most suitable classification technique with average accuracy between 5 groups of age is 98%, specificity of 0.79, the sensitivity of 0.9814 and 0.0185 of mean absolute error. SVM also give the lowest mean absolute error compared to RF and Naïve Bayes. This study is consistent with the previous work that there is a relationship between handgrip and age

Hazard control management on optimization layout of vent stack at offshore platform

The flaring is a normal practice in the oil and gas industry to achieve a safe and reliable process during the emergency situation. This situation is a routine practice for oil and gas production by controlled burning of natural gas. The burning process can cause hazards by explosion or at the very least surrounding environment will be affected by heat radiation during vent stack burning operation. Hence, investigation of the gas flaring produced by the vent stack is needed to tackle these problems. This paper presents designing a safe vent stack position in the limited space of oil and gas platform with considered the heat radiation produced by the vent stack. The simulation will be done by using flaresim software to predict the heat contour, heat radiation, and gas dispersion. The results proved that the optimal position of vent stack with water shield gives a better heat radiation

Investigation of human electromagnetic radiation characteristic for kidney disease patients

The human body is shown to have their own radiation that emits at certain frequencies into the space surround the body. The purpose of this paper is to investigate the characteristic of human electromagnetic radiation among kidney disease patients and non-kidney disease participants. The body radiation frequencies are measured using body radiation wave detector at twenty-two points of the human body. The properties of human electromagnetic radiation are evaluated using statistical analysis of dependent t-test of Wilcoxon Signed Rank test and independent t-test of Mann-Whitney U test. Significant results with the Sig. value below 0.05 are shown in lower body, torso, chakra, arm and upper body, thus indicates the characteristic differences of human electromagnetic radiation frequency between kidney disease patients and non-kidney disease participants

Feasibility Study of a Low Cost Saltwater Lamp for Rural Area

Renewable energy is energy generated from natural resources and cannot be depleted. Solar energy is the fastest growing source of renewable energy but the high installation and maintenance cost of a solar system has restrained the consumers from adopting this technology at their home or commercial building. This is especially true for those in developing countries. A new promising renewable energy source known as saltwater energy that takes advantage of the conductive nature of salt water to generate electricity, has intrigued many people. A study has been conducted to develop and produce saltwater-powered devices especially for rural and remote communities in Malaysia as well as worldwide. To main objective of this study is to determine the factors that affect the performance of the saltwater energy generation such as electrode’s combinations, number of cells and the durability of the electrodes. It was found that the choice of electrodes as anode and cathode does affect the voltage output. However, due to the small power produce, the number of cells must be increased to produce enough power to light up a led light and to provide power to USB port. This paper also conducted a cost analysis of using the saltwater lamp and compared it with a solar system. Although the difference in the cost per hour is very small, there are a number of disadvantages of solar system that need to be aware of. The findings obtained from these experiments will be used to design a prototype of the illumination technology for further product development