State estimation plays a vital role in the security analysis of a power system. The weighted least squares method is one of the conventional techniques used to estimate the unknown state vector of the power system. The existence of bad data can distort the reliability of the estimated state vector. A new algorithm based on the technique of quality control charts is developed in this paper for detection of bad data. The IEEE 6-bus power system data are utilised for the implementation of the proposed algorithm. The output of the study shows that this method is practically applicable for the separation of bad data in the problem of power system state estimation.
The present paper deals with the novel approach for clustering using the image feature of stabilization diagram for automated operational modal analysis in parametric model which is stochastic subspace identification (SSI)-COV. The evolution of automated operational modal analysis (OMA) is not an easy task, since traditional methods of modal analysis require a large amount of intervention by an expert user. The stabilization diagram and clustering tools are introduced to autonomously distinguish physical poles from noise (spurious) poles which can neglect any user interaction. However, the existing clustering algorithms require at least one user-defined parameter, the maximum within-cluster distance between representations of the same physical mode from different system orders and the supplementary adaptive approaches have to be employed to optimize the selection of cluster validation criteria which will lead to high demanding computational effort. The developed image clustering process is based on the input image of the stabilization diagram that has been generated and displayed separately into a certain interval frequency. and standardized image features in MATLAB was applied to extract the image features of each generated image of stabilisation diagrams. Then, the generated image feature extraction of stabilization diagrams was used to plot image clustering diagram and fixed defined threshold was set for the physical modes classification. The application of image clustering has proven to provide a reliable output results which can effectively identify physical modes in stabilization diagrams using image feature extraction even for closely spaced modes without the need of any calibration or user-defined parameter at start up and any supplementary adaptive approach for cluster validation criteria.
Cloud Computing provides a solution to enterprise applications in resolving their services at all level of Software, Platform, and Infrastructure. The current demand of resources for large enterprises and their specific requirement to solve critical issues of services to their clients like avoiding resources contention, vendor lock-in problems and achieving high QoS (Quality of Service) made them move towards the federated cloud. The reliability of the cloud has become a challenge for cloud providers to provide resources at an instance request satisfying all SLA (Service Level Agreement) requirements for different consumer applications. To have better collation among cloud providers, FLA (Federated Level Agreement) are given much importance to get consensus in terms of various KPI’s (Key Performance Indicator’s) of the individual cloud providers. This paper proposes an FLASLA Aware Cloud Collation Formation algorithm (FS-ACCF) considering both FLA and SLA as major features affecting the collation formation to satisfy consumer request instantly. In FS-ACCF algorithm, fuzzy preference relationship multi-decision approach was used to validate the preferences among cloud providers for forming collation and gaining maximum profit. Finally, the results of FS-ACCF were compared with S-ACCF (SLA Aware Collation Formation) algorithm for 6 to 10 consecutive requests of cloud consumers with varied VM configurations for different SLA parameters like response time, process time and availability.
The use of graphical knowledge representation formalisms with a representational vocabulary agreement of terms of conceptualization of the universe of discourse is a new high potential approach in the ontology engineering and knowledge management context. Initially, concept maps were used in the fields of education and learning. After that, it became popular in other areas due to its flexible and intuitive nature. It was also proven as a useful tool to improve communication in corporate environment. In the field of ontologies, concept maps were explored to be used to facilitate different aspects of ontology development. An essential reason behind this motivation is the structural resemblance of concept maps with the hierarchical structure of ontologies. This research aims to demonstrate quantitative evaluation of 4 different hypotheses related to the effectiveness of using concept maps for ontology conceptualization. The domain of Quran was selected for the purpose of this study and it was conducted in collaboration with the experts from the Centre of Quranic Research, Universiti Malaya, Kuala Lumpur, Malaysia. The results of the hypotheses demonstrated that concept mapping was easy to learn and implement for the majority of the participants. Most of them experienced improvement in domain knowledge regarding the vocabularies used to refer to the structure of organization of the Quran, namely Juz, Surah, Ayats, tafsir, Malay translation, English translation, and relationships among these entities. Therefore, concept maps instilled the element of learning through the conceptualization process and provided a platform for participants to resolve conflicting opinions and ambiguities of terms used immediately.
There are many variables involved in the real life problem so it is difficult to choose an efficient model out of all possible models relating to analytical factors. Interaction terms affecting the model also need to be addressed because of its vital role in the actual dataset. The current study focused on efficient model selection for collector efficiency of solar dryer. For this purpose, collector efficiency of solar dryer was used as a dependent variable with time, inlet temperature, collector average temperature and solar radiation as independent variables. Hybrid of the least absolute shrinkage and selection operator (LASSO) and robust regression were proposed for the identification of efficient model selection. The comparison was made with the ordinary least square (OLS) after performing a multicollinearity and coefficient test and with a ridge regression analysis. The final selected model was obtained using eight selection criteria (8SC). To forecast the efficient model, the mean absolute percentage error (MAPE) was used. As compared to other methods, the proposed method provides a more efficient model with minimum MAPE.
Since its debut in 2009, League of Legends (LoL) has been on a rise in becoming an extremely favoured multiplayer online battle arena (MOBA) game. This paper presented a logic mining technique to model the results (Win / Lose) of the LoL games played in 3 regions, namely South Korea, North America and Europe. In this research, a method named k satisfiability based reverse analysis method (kSATRA) was brought forward to obtain the logical relationship among the gameplays and objectives in the game. The logical rule obtained from the LoL games was used to categorize the results of future games. kSATRA made use of the advantages of Hopfield Neural Network and k Satisfiability representation. The data set used in this study included the data of all 10 teams from each region, which composed of all games from Spring Season 2018. The effectiveness of kSATRA in obtaining logical rule in LoL games was tested based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and CPU time. Results acquired from the computer simulation showed the robustness of kSATRA in exhibiting the performance of the LoL teams.
Clonal selection algorithm and discrete Hopfield neural network are extensively employed for solving higher-order optimization problems ranging from the constraint satisfaction problem to complex pattern recognition. The modified clonal selection algorithm is a comprehensive and less iterative immune-inspired searching algorithm, utilized to search for the correct combination of instances for Very large-scale integrated (VLSI) circuit structure. In this research, the VLSI circuit framework consists of Boolean 3-Satisfiability instances with the different complexities and number of transistors are considered. Hence, a hybrid modified clonal selection algorithm with discrete Hopfield neural network is well developed to optimize the configuration of VLSI circuits with different number of electronic components such as transistors as the instances. Therefore, the performance of the developed hybrid model was assessed experimentally with the standard models, HNNVLSI-3SATES and HNNVLSI-3SATGA in term of circuit accuracy, sensitivity, robustness and runtime to complete the verification process. The results have demonstrated the developed model, HNNVLSI-3SATCSA produced a minimum error (consistently approaching 0), better accuracy (more than 80%) and faster computational time (less than 125 seconds) against changes in the complexity in term of the number of transistors. Furthermore, the developed hybrid model is able to minimize the computational burden and configurational noises for the variant of VLSI circuits.
Interactions between multispecies are usual incidence in their habitats. Such interactions among the species are thought to be asymmetric in nature, which combine with environmental factors can determine the distributions and abundances of the species. Most often, each species responds differentially to biotic interactions and environmental factors. Therefore, predicting the presence-absence of species is a major challenge in ecology. In this paper, we used mathematical modelling to study the combined effects of biotic interactions (i.e. asymmetric competition) and environmental factors on the presence-absence of the species across a geographical region. To gain better insight on this problem, we performed invasion and numerical simulation analyses of the model of multispecies competitive dynamics. Different threshold values of competition coefficients were observed, which result in different phenomena; such as coexistence of species and priority effects. Consequently, we propose that asymmetric biotic interactions, combined with environmental factors can allow coexistence of relatively weak and strong species at the same location x.
Dengue fever (DF) is a global health problem and considered to be endemic in Malaysia. Conventional mosquito traps currently applied as vector control do not effectively reduce Aedes mosquito population. AedesTech Mosquito Home System (AMHS) is an autocidal ovitraps for Aedes mosquitoes that uses the ‘lure and kill’ concept and is expected to be able to reduce Aedes mosquito population. The effectiveness of AMHS in reducing Aedes mosquito population was investigated in Block A, B and D (control) of the 17th College, Universiti Putra Malaysia (UPM). For the first two weeks (pre-intervention), the conventional ovitraps were used to obtain the initial abundance of mosquito population in Block A, B and D. Subsequently, AMHS was used for the next three months and again followed by the conventional ovitrap for the final two weeks (post-intervention). Ovitrap Index, Hatching Index and percentage of emergence of adult mosquitoes were calculated once every two weeks. Data were analysed using Paired Sample T-test. Values were considered significant at p≤0.05. The Ovitrap Index that indicates the mosquito population at Block A and B was significantly higher (p≤0.05) than of Block D. Hatching Index of AMHS was significantly lower (p≤0.05) then conventional ovitraps. All mosquito eggs collected in AMHS did not develop into adult mosquitoes. There was a significant reduction (p≤0.05) in the mosquito population between the pre- and post-intervention. In conclusion, AMHS was effective in reducing the mosquito population in 17th College, UPM. Therefore, it is believed to be a very promising vector management option to control the incidence of DF.
The potential of Moringa oleifera Lam. (Moringaceae) and Centella asiatica (L.) Urban (Apiaceae) extracts (TGT-PRIMAAGE) in slowing the decline of memory and learning activity was investigated using D-galactose-induced ageing rat model. The extracts were profiled and standardised based on markers identified using LC/MS-QTOF. Toxicity study of the extract was done, and the rat did not show any sign of toxicity. The extract was orally administered to the rat and dose dependent (100, 500 and 1000 mg/kg) efficacy were investigated. The rats were subjected to Morris Water Maze whereby 3 parameters were studied (number of entry to platform, latency and novel object recognition). Plasma was collected for the determination of catalase (CAT) activity and levels of malondialdehyde (MDA) and advanced glycation end products (AGEs). The activity of acetylcholinesterase (AChE), level of acetylcholine (ACh) and lipid peroxidation (LPO) were measured using the brain lysates. Significant improvement (p < 0.05) was seen in the memory and learning abilities in the aged rats that received medium and high dose of TGT-PRIMAAGE, and tocotrienol. Rats treated with TGT-PRIMAAGE had also shown improved CAT activity and resulted in reduced LPO. The level of ACh was found increased in parallel with the reduced AChE activity. The capabilities of learning and memory of the TGT-PRIMAAGE treated rats were enhanced via inhibition of AChE activity and subsequently increased level of ACh.
Gallic acid (GA) is a phenolic compound found in almost all plants and has been reported to possess powerful health benefits such as anti-oxidant, anti-inflammatory, anti-cancer, and anti-diabetic properties. However, GA suffers a short half-life when administered in vivo. Recent studies have employed graphene oxide (GO), a biocompatible and cost-effective graphene derivative, as a nanocarrier for GA. However, the toxicity effect of this formulated nano-compound has not been fully studied. Thus, the present study aims to evaluate the toxicity and teratogenicity of GA loaded GO (GAGO) against zebrafish embryogenesis to further advance the development of GA as a therapeutic agent. GAGO was exposed to zebrafish embryos (n ≥ 10; 24hr post fertilization (hpf)) at different concentrations (0-500 μg/ml). The development of zebrafish was observed and recorded twice daily for four days. The toxicity of pure GO and GA was also observed at similar concentrations. Distilled water was used as control throughout the experiment. A significantly high mortality rate, delayed hatching rate and low heartbeat were recorded in embryos exposed to GO at concentrations of ≥ 150 μg/ml at 48 hr (p
High-intensity exercise acutely improves suppression of appetite in populations with normal body mass index (BMI). However, whether moderate intensity exercise (MIE) and high-intensity exercise (HIE) can elicit similar (or greater) appetite suppression effects for obese populations are still relatively unknown. The main aim is to investigate the acute effects of MIE and HIE on the appetite score, eating behaviour and blood glucose regulation among the obese population. Twelve obese participants (age: 20.8 ± 1 yr, BMI: 34.1 ± 3 kg·m-2, V̇o2max: 30.7 ± 3 ml·kg·min-1) were randomly allocated, in a crossover manner, with a 7-day interval in between (1) MIE (cycling at 60-75% HRmax), (2) HIE (cycling at 80-95% HRmax, 8-sec sprint x 12 sec rest) and (3) control (CON) condition after a 10-hr overnight fast. Physiological (fasting blood [glucose] and 24-hr calorie intake) and psychological responses (Three Factor Eating Questionnaire-R18, TFEQ-R18, and appetite score using Visual Analog Scale, VAS) were recorded prior to and after exercise interventions. Both MIE and HIE significantly reduced the calorie intake compared to CON (P0.05). A difference was found in fasting blood [glucose] level between trials in MIE (P0.05). In response to acute intervention, both MIE and HIE improved some psychological appetite score and attenuated daily energy consumption; these positive effects could benefit obese and diabetic populations.
The tone of peking 1, 2, 3, 5, 6, 1’ was investigated using time-frequency analysis (TFA). The frequencies were measured using PicoScope oscilloscope, Melda analyzer in Cubase version 9 and Adobe version 3. Three different approaches for time-frequency analysis were used: Fourier spectra (using PicoScope), spectromorphology (using Melda analyzer) and spectrograms (using Adobe). Fourier spectra only identify intensity-frequency within entire signals, while spectromorphology identify the changes of intensity-frequency spectrum at fixed time and Adobe spectrograms identify the frequency with time. PicoScope reading produces the spectra of the fundamental and overtone frequencies in the entire sound. These overtones are non-harmonic since they are non-integral multiples of the fundamental. The fundamental frequencies of peking 1, 2, 3, 5, 6 were 1066Hz (C6), 1178Hz (D6), 1342Hz (E6), 1599Hz (G6) and 1793Hz (A6) respectively while peking 1’was 2123Hz (C7) i.e. one octave higher than peking 1. Melda analyzer reading proved that all peking sustained the initial fundamental frequency and overtone at t=0 until 2s. TFA from Adobe reading provides a description of the sound in the time-frequency plane. From TFA, peking 1, 2 and 6 exhibited a much gentler attack and more rapid decay than peking 3, 5 and 1’.
Previous studies have indicated that the pipe-surface-mounted helical strakes effectively reduce vortex-induced vibration (VIV) under a uniform flow application, particularly during the lock-in region. Since VIV experiments are time-consuming, observation is generated with an interval helical strakes parameter in pitch and height to lessen tedious procedures and repetitive post-processing analyses. The aforementioned result subset is insufficient for helical strakes design optimisation because the trade-off between the helical strakes dimension, lock-in region and flow velocity are non-trivial. Thus, a parametric model based on an improved recursive least squares (RLS) parameter estimation technique is proposed to define the statistical relationship between input, or strakes and pipe dimension, and output, or VIV amplitude ratio. As results suggested, revised RLS estimated VIV model demonstrated an optimal prediction with the highest coefficient of determination and lowest Integral Absolute Error. The feasibility of VIV parametric model was validated by embed into Genetic Algorithm (GA) as the fitness function to acquire a desirable helical strakes dimension with minimum VIV amplitude. The rapid generation of optimal helical strakes dimension which returned the highest VIV suppression implied a superior simulation method compared to the experimental outcome.
Maximum k Satisfiability logical rule (MAX-kSAT) is a language that bridges real life application to neural network optimization. MAX-kSAT is an interesting paradigm because the outcome of this logical rule is always negative/false. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when the logical rule is embedded in HNN. Increasing the storage capacity during the learning phase of HNN has been a challenging problem for most neural network researchers. Development of Metaheuristics algorithms has been crucial in optimizing the learning phase of Neural Network. The most celebrated metaheuristics model is Genetic Algorithm (GA). GA consists of several important operators that emphasize on solution improvement. Although GA has been reported to optimize logic programming in HNN, the learning complexity increases as the number of clauses increases. GA is more likely to be trapped in suboptimal fitness as the number of clauses increases. In this paper, metaheuristic algorithm namely Artificial Bee Colony (ABC) were proposed in learning MAX-kSAT programming. ABC is swarm-based metaheuristics that capitalized the capability of Employed Bee, Onlooker Bee, and Scout Bee. To this end, all the learning models were tested in a new restricted learning environment. Experimental results obtained from the computer simulation demonstrate the effectiveness of ABC in modelling MAX-kSAT.
Gamelan in general is categorized as a group of gongs. This traditional Malay gamelan ensemble is in a slendro scale i.e. five notes per octave. The rhythms, pitch, duration and loudness classify the various groups of gongs such as bonang, kenong, gender, peking and gambang. The cast bronze peking, kenong and bonang were chosen from a range of Malay gamelan ensemble from Universiti Malaysia Sarawak (UNIMAS), Universiti Putra Malaysia (UPM), Universiti Kebangsaan Malaysia (UKM) and Universiti Teknologi Mara (UiTM). The sounds were recorded by PicoScope Oscilloscope. The PicoScope software displays waveform and spectrum in time and frequency domain respectively. The peking lowest and highest frequencies from UiTM were 293 Hz and 1867 Hz, from UPM were 644 Hz and 1369 Hz, from UKM were 1064 Hz and 2131 Hz and from UNIMAS were 1072 Hz and 2105 Hz respectively. The kenong lowest and highest frequencies from UiTM were 259 Hz and 463 Hz, from UPM were 294 Hz and 543 Hz, from UKM were 300 Hz and 540 Hz and from UNIMAS were 293 Hz and 519 Hz respectively. The fundamental frequencies of bonang from UPM were higher than that of UKM, UiTM and UNIMAS. The harmonics were not successive but interrupted by another frequency. The harmonics of each bonang was similar except for gamelan from UKM.
This study aims to evaluate a continuous flow model that involves a ramp area at kilometer 31.6 on the highway from Shah Alam to Kuala Lumpur, to analyze the findings of numerical results of instantaneous speed ratios and to observe the convergence patterns for each section. The continuous flow model assumes traffic flow to be similar to the heat equation in regard to the concept of the one-dimensional viscous flow of a compressible fluid. For the methodology, for solving an initial value-boundary value problem, an initial condition together with a set of boundary conditions are required to solve the partial differential equation. The boundary conditions are chosen to assess the suitableness of the design of the entrance ramp in Malaysia, which is for right hand drive traffic. Highway traffic data were collected on the tapered acceleration lane and obtained by the videotaping method. The Maple programming language was used to write a numerical code in order to evaluate the instantaneous speed ratio in terms of a Fourier series. Our results show that the realistic results of instantaneous speed ratios on the ramp at kilometer 31.6 from Shah Alam to Kuala Lumpur are acceptable when compared to the theoretical results. Therefore, a very minimal collision rate is expected due to the well-designed ramp at kilometer 31.6 from Shah Alam to Kuala Lumpur. It is beneficial to study the mathematical model and theories of traffic flows on the merging area to enhance the efficiency of the traffic flowing on highways.
Solar drier is considered to be an important product used in the internet of things (IoT). It is used to dry different kinds of products used in agriculture or aquaculture. There are many factors that have different effects on the drying of items in the solar drier. The current study focused on the removal of the moisture ratio in the drying process for seaweed using solar drier. For this purpose, a dataset containing 1924 observations was used to study the effect of six different independent variables on the dependent variable. Moisture ratio removal (%) was considered to be dependent variable with ambient temperature, chamber temperature, collector temperature, chamber relative humidity, ambient relative humidity and solar radiation as independent variables. All possible models were used in the analysis till fifth order interaction terms. Hybrid model of LASSO with bisquare M was proposed for efficient selection of the model. The procedure based on four phases was used for efficient model selection and a comparison was made with other existing sparse and robust regression techniques. The result indicates that the proposed technique is better than other existing techniques in terms of mean squared error (MSE) and mean absolute percentage error (MAPE).
The assessment of model fit is important in Structural Equation Modeling (SEM). Several goodness-of-fit (GoF) measures are affected by sample size and the number of parameters to be estimated. A large sample size is needed to test a complex model involving a large number of parameters to be estimated. One of the solutions to reduce the number of parameters to be estimated in a given model is by considering item parceling. The effects of item parceling on parameter estimates and GoF measures in a structural equation model was investigated via a simulation study. The simulation results indicate that the parameter estimates are closer to the true parameter values for the IL model whenever the distribution of data is normal but biased when the data is highly skewed. The parameter estimates for the IP model were found to be underestimated for both normal and non-normal data. The GoF measures were higher for the IP model. Additionally, the RMSEA was lower for the IP model when data were skewed. This shows that item parceling may improve GoF measures but the effect of exogenous on endogenous variable is underestimated. Application to a real data set confirmed the results of the simulation study.
Fine resolution (hourly rainfall) of rainfall series for various hydrological systems is widely used. However, observed hourly rainfall records may lack in the quality of data and resulting difficulties to apply it. The utilization of Bartlett-Lewis rectangular pulse (BLRP) is proposed to overcome this limitation. The calibration of this model is regarded as a difficult task due to the existence of intensive estimation of parameters. Global optimization algorithms, named as artificial bee colony (ABC) and particle swarm optimization (PSO) were introduced to overcome this limitation. The issues and ability of each optimization in the calibration procedure were addressed. The results showed that the BLRP model with ABC was able to reproduce well for the rainfall characteristics at hourly and daily rainfall aggregation, similar to PSO. However, the fitted BLRP model with PSO was able to reproduce the rainfall extremes better as compared to ABC.