This paper present experiment made to compare the charging and discharging capability between lithium-ion battery and super-capacitor for slow charge rate. The lithium ion battery and super-capacitor were both discharge to 0% state of charge, SOC and charged up to 63% SOC. DC supply and photovoltaic solar panel as the power source for charging. The trends in charging characteristics for both energy storage devices were observed. It is observed that the super-capacitor can act as an energy storage based on the charge and discharge pattern from the experiment.
This paper describes the density of traffic forecasting based on information obtained from social media. Social media twitter can become a source of accurate information as it comes from the verified twitter account i.e police department of republic Indonesia. The tweet (information) from this account is saved in our database. This research is expected to obtain results in the form of a system design and implementation of data traffic forecasting using LVQ algorithms. The result of this research is the capability of the system to give to the mobile device, the traffic information forecasting.
To help expedite the process of constructing use case diagrams, a widely used notation in software engineering, we attempt to develop a generator system that can extract use case diagrams automatically from the input of a software requirements specification. The use of natural language processing techniques can greatly assist this process, one of which is to use syntax-driven semantic analysis. Semantic analysis can provide output in the form of semantic representations that can be used to extract appropriate use case elements. A set of rules have been developed to extract information about the elements of use case diagrams contained in the semantic representation. Our tests show that the system is able to automatically construct use case diagrams for a wide variety of linguistic variations. In a test using real-world cases, an average precision of 0.7375 and recall of 0.691 is obtained.
Energy, both in terms of its production and its usage has occupied a prime place in research as well as politics and world economy for the past few years. The majority of nations are aiming to deliver severe carbon cuts in the next few years. However, achieving a carbon-free future needs more than infrastructure investment and novel efficient technologies for buildings, transportation and other large consumer domains. It needs a better understanding of people as consumers, as well as a better understanding of energy waste across the multitude of socio-techical systems around us.With regards to the built environment and particularly residential buildings, the authors propose that dual, quantitative and qualitative approaches to characterising, assessing and improving occupied buildings are necessary. Such approaches would synchronously cater for understanding i) buildings technical performance (fabric and building heating, cooling and ventilation systems) and ii) occupant's motivation, ability, knowledge and efficacy for adopting low carbon lifestyles. When deployed at scale, the above will enable cost effective, targeted interventions for both building fabric and systems improvement and towards empowering their occupants to live sustainably.The paper describes such a quantitative and qualitative approach and proposes assessment tools. Further, the authors comment on the potential benefits from monitoring campaigns when deployed at scale.
This research develops the concept of CBIR on the image motif. For processes that do not only find images that have been stored in database, but also be able to recognize some resemblance ornament image or texture as well as form. Although different size, direction of slope, and the layout of texture and shape, but the concept will be recognized. In calculating the percentage of similarity is not only based on performance measurement precision but also the image of the relevant. From the results of over 250 studies batik motif images and 25 images in the database query, it is used for texture feature extraction methods canny edge detection and shape invariant moment feature extraction. For the calculation of the similarity distance and Canberra distance is used euclid functions. Threshold Algorithm which will display the image based on the value of the highest grade representation on each image query, followed by comparing the results of feature extraction using the operator min on fuzzy logic to generate maximum value. Excess Threshold algorithm, compared with other methods lies in simplicity retrieval method in the image, so that the performance of CBIR becomes more reliable and effective.
Fetal head detection and approximation from ultrasound image is an important method in obstetric and gynaecology. In this research, we propose a modification of an efficient algorithm to detect an ellipse shape in an image. Our proposed method is using an efficient way to approximate an ellipse based on its minor axis. By enumerating every possible minor axis from a pairs of pixels, other ellipse parameter can be estimated. For verifying the ellipse approximation result, a voting mechanism is conducted to vote the most appropriate set of parameters for an ellipse. Instead of using every edge pixels in the image, we randomize the pixels to gain speed improvement. We test the algorithm using two different data. The first one is real ultrasound image and the second one is synthetic image which has been populated with salt noise. The ultrasound image is cleaned from speckle noise using Speckle Reducing Anisotropic Diffusion (SRAD) algorithm. The experiment gives satisfying result in both of synthetic and real images.
Batik, as a cultural heritage from Indonesia, has a lot of motifs based on certain patterns. This paper discusses feature extraction methods for the recognition of batik motifs in digital images. In this study, the use of several feature extraction methods have been compared in terms of their performance with several scenarios for testing level accuracy. The methods include Gray Level Co-occurrence Matrices (GLCM), Canny Edge Detection, and Gabor filters. The experimental results show that the use of GLCM features has performed the best with a classification accuracy reaching 80%.
This study presents a novel method for controlling the effect of outdoor illumination on object color hues by using an external white LED illumination to adapt to changing outdoor illumination. Outdoor illumination varies with shadows and cloud movements passing the sunlight. Gradual or sudden changes in illumination in outdoor applications influence image hues and colors, which, in turn, affect the intended classification results. In this study, graphs show that the images taken at 10.00am with external white LED illumination exhibited a similar curve pattern with images taken at 12.00pm whereas for images taken without white LED illumination displayed an individual curve pattern at different capture times. This suggests that the external white LED illumination provided a color correction effect against gradual illumination changes in outdoor application.
Internet is an important component in technology development, including tourism. Tourism is an industry which involves much information needed for traveling. Many tourists search for travel information in the web. Nevertheless, the web often gives irrelevant information. Besides that, a huge size of information in the web and the spread of the information in many different sources make users need more time in searching for information and organizing them from many different sources manually. Semantic web is a solution to solve those problems by providing knowledge based on an ontology. E-tourism is a good domain for implementing the semantic web because there are many information sources and data exchange involved in the e-tourism. In this paper, a knowledge base which is based on an ontology is designed and built using Protege tool version 3.4.7. The ontology consists of tourism domain-specific information and stores data of accommodation, attraction, and cultural event in Bali which is one of the main travel destinations in Indonesia. Besides that, a search engine application for e-tourism in Bali, which implements the semantic web, is designed and built using RAP-RDF version 0.9.6 and RDF query language: SPARQL so that the search results conform to the ontology.
Molecular dynamics simulation is a simulation modeling of proteins and some chemical compounds in the pharmaceutical field. Molecular dynamics simulations are used as a way for drug discovery. This paper is going to propose about cloud computing model of molecular dynamics simulations using Amber and Gromacs. Cloud computing applications can be used as a bridge between molecular dynamics applications running on parallel computing and a multiplatform client, so that end-users can use the applications of molecular dynamics simulations easily.
This research proposed a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research used Probabilistic Neural Network (PNN) classifier for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identifying and finding information about Indonesian medicinal plant.
Extreme Programming (XP) is a widely used method for software development. This method is used to improve the quality of software. However, the use of XP is limited to small and medium organization. Therefore, Industrial Extreme Programming (IXP) is developed to meet the needs of larger organization as an evolution of XP. However, IXP is not accompanied by complete procedures and tools needed by developer. Rational Unified Process (RUP) comes as a software development process that is flexible to other framework to be applied in. RUP provides clear steps and responsibilities in the development of software. We propose the framework to get a method that suitable for large organization and provide convenience to developers by combining IXP practice and RUP.
QR Code Augmented Reality (QRAR) is an Augmented Reality does not require pre-registration, it has 10(7089) combination ID-encoded and can be used on the public AR application. The results from previous research are 6 DOF tracking method less accurate, require small computation power and unstable marker. We propose merging conventional marker with QR Code, but it will have noise on the QR Code Finder Patter (QRFP) under perspective distortion, so we propose a Backpropagation method to keep detecting the QRFP and the method preceded by feature extraction with low level image processing. The methods we have proposed, achieve accurate 6 DOF, runs at 35.41 fps and stable marker as conventional marker.
This paper presents a paddy growth stages classification using MODIS remote sensing images with support vector machines (SVMs). We collected the paddy growth stages data samples from a series of MODIS mages acquired from March to July 2012 along paddy field area only. The data are collected based on growth stages phenology of paddy using spectral profile which consists of at least 9 classes for growth stages and 2 classes for dominated soil and cloud. We apply SVMs to build a binary classifier for each class with one against all strategy of multiclass approach. One important issue needed to address is unbalanced prior probability that should be solved by each SVM. In this study, we evaluate the effectiveness of balanced branches strategy that is applied to one against all SVMs learning. Our results shows that the balanced branches strategy does improves in average around 10% classification accuracy during training and validation, and in average around 50% during testing.
Interferometer sensor has 4 quadrants on it which can be used to test the effects of movement in a structure. This movement will be tested in 3 dimensions. The test undertaken will be a laboratory test using a miniature model of a structure and moving in the axis of X, Y and Z. By doing so, the result gathered will be concluded whether it will function in real world application which will be in a real scale. This technology is suitable for this application due to the nature of the interferometer sensor which is accurate and fast at the speed of light. With the interferometer capability, monitoring of the structure can be made in real time. Its application are not limited to monitoring but also use to prevent from structure collapse, deaths caused by collapsed structure as well as for research into the limit of structure movement or bend before it collapses.
The k-principal points of a distribution are the k points that optimally partition the distribution. In this paper, we propose a method to estimate principal points from data by using mixture distributions when we have no prior knowledge of the distribution of data. Several simulation results are presented to compare the proposed method with the nonparametric k-means.
This paper presents the development of wireless sensor monitoring system for environmental applications. The system is based on wireless ZigBee technology and uses 32-Bit Arduino Uno microcontroller for monitoring of environmental parameters measurements online such as carbon dioxide, oxygen, temperature and humidity levels. The CO2, O2, humidity, and temperature sensors are integrated into the data acquisition system. Data was collected at a certain location at Universiti Sains Malaysia, consistency models are define for analyzing the quality of data and the level of carbon dioxide and oxygen in the deployed environment. The results show that the system is capable of monitoring and analysis of CO2 and O2 in the deployed environment and this success shows the potential of this system for application in environment where reliable gas monitoring is crucial.
Localization schemes have a significant role in wireless sensor networks. During the randomly deployment in an untouched area, the nodes has to be capable for self-management to determine their position. The node could estimate the position based on the received signal from reference nodes or receiving packet information contains the coordinate of reference nodes and hop count. Considering the efficiency of nodes capability on distance measurement, we propose a hybrid localization algorithm, called H-Loc, which is a combination of DV-Hop algorithm and Received Signal Strength (RSS) method. This algorithm is useful on distance measurement selection based on the node position towards their references. The proposed algorithm enhances the localization accuracy compared with the previous algorithms, which has been demonstrated by the simulation result.
This paper presents the development of a fuzzy model for classification of paddy growth stages based on synthetic MODIS data. Classification of growth stages is an important process in prediction of crop production using a remote-sensing technology. The proposed approach takes advantages of the nature of a fuzzy system which is able to capture gradual changes/movements by fitting its membership functions. A novel approach to shaping fuzzy input membership functions based on box-plot parameters is also presented. The developed fuzzy model was build and tested on 3935 sets of synthetic MODIS data. The results show that the proposed method was able to classify the growth stages satisfactorily and was robust to handle noises in the data.
There are a number of ways to monitor traffic and help people to navigate through or avoid traffic jams. A prospective way is to use smart phones with GPS enabled device as traffic sensors, which complement existing sensors. This paper attempts to highlight a number of progressive steps in the effort to build an integrated ITS, which harnesses smart phones as intelligent agent. However, a number of questions should be addressed first: How smart phones can avoid map mismatching phenomenon which is a common problem in navigation devices ? What if there are compromised agents which attempt to invalidate the gathered data ? and how to place detectors in such a system. Consequently, there are three possible solutions discussed in this paper: the use of non-overlapping zones in Virtual Detection Zone (VDZ), filtering algorithm to ignore compromised agents and the use of macroscopic simulation to aid the placement of VDZ in selected roads.
