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Temperature Compensation for Transformer-type Transducer
(2021-01-01)
Songsuwankit, Kanoknuch
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Riewruja, Vanchai
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Watanachaturaporn, Pakorn
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Rerkratn, Apinai
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Petchmaneelumka, Wandee
A novel technique to compensate the temperature effect of a transformer-type transducer is proposed in this paper. The effect of the ambient temperature on the transformer-type transducer is investigated from a primary-winding current. The advantage of the proposed technique is that the temperature effect is compensated without requiring a temperature sensor, making it suitable for applications in robotic and automation systems operated in harsh environments. The primary-winding current of the transducer is generated using a second-generation current conveyor (CCII). The excitation signal of the transformer-type transducer is driven by the CCII and the current flowing through the primary winding is transferred to an output signal of the CCII. The deviation of the primary-winding current due to the temperature effect is evaluated from the output signal of the CCII. The temperature effect on the transducer is manipulated by a closed-loop principle using a subtract-and-sum action instead of a traditional proportional-plusintegral action to eliminate the deviation of the primary-winding current. Therefore, the temperature effect on the transducer is compensated. A linear variable differential transformer (LVDT) is used to demonstrate the proposed technique, whose performance is discussed in detail and confirmed experimentally. All devices used in this experiment are commercially available. Experimental results show that the measured error of the output signal from the LVDT at 70 C can be reduced from 6.2% without temperature compensation to 0.06% by using the proposed technique, which has the advantages of a low cost, simple configuration, and high performance.
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A Design and Implementation of an Emergency Message Beacon System Using APRS Protocol
(2020-06-01)
Hongyim, Nimit
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Watanachaturaporn, Pakorn
A design and implementation of an Emergency Message Beacon System (EMBS) using the Automatic Position Reporting System (APRS) protocol is presented in this paper. The EMBS benefits from widely distributed radio network where modern wireless network infrastructure is unavailable such as on open seas or in the middle of jungle. The main concept of the design is that the system an immediate beacon containing current location is transmitted when activates the unit and automatically sends interval beacons to report location, system status, and an instant message. The unit works with either an existing or impromptu APRS ad-hoc networks. The system is designed to transmit low RF power and expected a minimum coverage of at least two kilometers in radius, which is the worst situation case of Line of Sight (LOS) communication. The experimental result shows that the unit can establish radio connection to another station at a range of 2.1 kms. Link budget analysis is theoretically validated the communication.
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Home appliances-controlled platform with homekit application
(2019-07-01)
Vongchumyen, Charoen
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Torthitithum, Sarayuth
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Khamsopa, Jirachaya
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Watanachaturaporn, Pakorn
Home appliances-controlled platform with HomeKit application is a platform that allows users to control general appliances via HomeKit, which is an application from Apple on any device running iOS10 or later. The platform is the communication medium between application and appliances. The appliances do not need to be manufactured by Apple since they are expensive and have to put an effort in case the customer want to buy it. The platform is capable of receiving and transferring data to a HomeKit application for data processing, then transmits signal back to control the operation of the appliances. Within the platform, there is a section that acts as a communication server for the HomeKit application which is like a direct Apple server. In addition to receiving and sending data from the HomeKit application, the platform also has an automatic control that is controlled by the reading sensor contained in each platform comparing the threshold values, user defined. Each platform has different sensors based on their operations and can control only one type of appliance because each type of control uses different signal, the control must be unique, matching the type of appliances and the form of command in different manufacturers.
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Vehicle Logo Detection Using Sliding Windows with Sobel Edge Features and Recognition Using SIFT Features
(2019-07-01)
Benjaparkairat, Jatupon
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Watanachaturaporn, Pakorn
Automated traffic monitoring is one of key component for a smart city due to its high efficiency and availability compared with human based monitoring. License plate recognition systems are widely used; however, information such as vehicle make are also required and still lack a practical method. Therefore, this paper proposes a practical method for vehicle logo detection and recognition in concern with a real-life surveillance situation. Instead of locating a logo from an entire image as several published proposals, sliding window method is proposed to locate candidate areas where a vehicle logo resided. The area is identified by the maximum number of Sobel edges compared among the candidate areas. The logo in the identified area is recognized using the SIFT based features and a Nearest Neighbor classifier. The proposed method is experimented with real-life traffic video surveillance images. The images are low resolution under various daylight condition. The proposed method is trained and experimented with 3,176 images of nine vehicle makes. The proposed method is assessed using confusion matrixes and shows overall accuracies in range of 85%.
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Door lock system via web application
(2017-10-19)
Vongchumyen, Charoen
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Watanachaturaporn, Pakorn
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Jinjakam, Chompoonuch
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Watcharapupong, Akkradach
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Kasemsiri, Watjanapong
This research proposes the smart way to solve the common problem of the conventional door-locking system. The important problems of the ordinary door-locking systems are key lost, forgetting to lock the system and cannot check the lock status from remote. To solve these problems, we proposed a method to lock-unlock the system which utilizes the web application and passcode alongside the usual method of using the ordinary keys. The proposed system can remotely lock, remotely unlock, remotely check door state, remotely check door locking state, send the email to the owner in case of someone knocking on the door and generate the temporary passcode to use in specific date and time.
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Empirical experiment on origin destination estimation using automatic licence plate matching on a rural road of Thailand
(2017-10-19)
Watanachaturaporn, Pakorn
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Chalermwongphan, Karn
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Huntajidkul, Achaya
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Vongchumyen, Charoen
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Watthanakul, Jettarin
The growth of economic influences city authorities to plan and build infrastructure. The growth prediction mostly uses modern mathematical models. The accuracy of the model is highly depended on reliable parameters. Two crucial parameters for most models are traffic capacity and average travel speed along road networks and obtained from an origin-destination (OD) estimation. Thus, an OD estimation is necessary to obtain reliable parameters for the predictive models. Traditionally, the OD estimation is performed by human observation; however, an automatic license plate recognition and matching algorithm show potential of obtaining such data. Additionally, the observation is able to perform long term observation which is impractical for human. Therefore, this paper reports an empirical experiment on OD estimation using automatic license plate matching along with a real-world human observation survey in Mukdahan province. The results from both types of surveys are reported and compared. The different results between the both raises an opportunity for researching an optimal method of OD estimation in Thailand.
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A framework for origin-destination estimation using license plate recognition for Thai rural traffic
(2017-07-01)
Praphananurak, Kitipong
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Sanghitkul, Winai
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Chovichien, Voraniti
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Watanachaturaporn, Pakorn
Origin-Destination (OD) estimation is a crucial parameter for planning city transportation network. Classical methods for collecting the OD information including individual interviewing or using pressurized tube counting equipment; such example methods are practically limited by the number of people involved during observation and interview, traffic delay, budget, and short observation period. Accuracy from human observation and short observation period are doubtfully accurate. Using image processing technology, the OD estimation can be performed for longer period by analyzing vehicle movement from recorded video or online video feeds. Moreover, an individual average speed for each vehicle or type of vehicle are obtainable from identify the license plate number. This paper proposes of a framework for applying a license plate recognition technology for OD estimation for Thai rural traffic. The proposed framework obtains trustable estimation for each individual vehicle. The results can be verified later by human or other methods from recorded video. Therefore, higher accurate and trustable estimation is obtained and make available for later usage.
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Identification of rice using symbolic regression
(2017-02-23)
Watanachaturaporn, Pakorn
Image classification is a preferred method for agriculture product identification since the product is intact from the process. Many published works have adopted image classification techniques for identifying rice seed varieties. Based on a classification algorithm and given sample features vectors, a classification model is built from a set of optimal parameters and operators. However, the obtained parameters rarely reflect relations among the feature symbolically and, therefore, are meaningless to human. This paper proposes of using a symbolic regression algorithm to search for possible identification solutions. A number of possible solutions and the associated analytical expressions are obtained. As a case study two classifiers are built from two chosen solutions and are applied to identify high quality Jasmine rice, the Khao Dawk Mali 105, from other three alike varieties. The performance of the classifiers is compared with the published work using percent identification accuracy. The experimental results show that the two classifiers obtained from a symbolic regression algorithm accurately identify the Khao Dawk Mali 105 at 86.25% and 90.00% while the compared algorithm accurately identify the rice in the range of 73.85%-83.46%. The most advantage of using a symbolic regression algorithm is that the algorithm also reveals analytical expressions. Such expressions suggest human a potential methodology concealed in the observed data.
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Implementation of a human vital monitoring system using ad hoc wireless network for smart healthcare
(2011-12-01)
Insom, Poramin
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Wongpanitlert, Pakorn
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Tipsupa, Jakree
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Rakjang, Kritsakorn
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Kaemarungsi, Kamol
A human vital monitoring system using Ad Hoc wireless network is implemented to enhance the healthcare service. Issues related to hardware and software implementation including the data packet format are addressed. The system uses a low cost electrocardiogram (ECG) sensor to monitor patient's vital sign. A sensor node, which is held by the patient, transmits the sensor data via an Ad Hoc wireless network to a gateway. The received data is processed and displayed in a form of graph on a monitoring screen. The system allows medical personal to check the status of patient remotely. Too the system is designed such that, if it detects anomaly signs from the patient, the sensor node can report an alarm to the monitoring screeen so that medical personal can assist the patient immediately. The system is developed using a real-time operating system and deployed an Ad Hoc routing protocol to reroute the packet to the gateway automatically. © 2011 IEEE.
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Multisource classification using support vector machines: An empirical comparison with decision tree and neural network classifiers
(2008-01-01)
Watanachaturaporn, Pakorn
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Arora, Manoj K.
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Varshney, Pramod K.
Remote sensing image classification has proven to be attractive for extracting useful thematic information such as landcover. However, often for a given application, spectral information acquired by a remote sensing sensor may not be sufficient to derive accurate information. Incorporation of data from other sources such as a digital elevation model (DEM), and geophysical and geological data may assist in achieving more accurate land-cover classification from remote sensing images. Recently, support vector machines (SVM) have been proposed as an alternative for classification of remote sensing data, and the results are promising. In this paper, we employ the SVM algorithm to perform multi-source classification. An IRS-1C LISS III image along with normalized differenced vegetation index (NDVI) image and DEM are used to produce a land-cover classification for a region in the Himalayas. The accuracy of SVM-based multi-source classification is compared with several other non-parametric algorithms namely a decision tree classifier, and back propagation and radial basis function neural network classifiers. The well-known kappa coefficient of agreement is used to assess classification accuracy. The differences in the kappa coefficient of classifiers have been statistically evaluated using a pairwise Z-test. The results show a significant increase in the accuracy of the SVM based classifier on incorporation of ancillary data over classification performed solely on the basis of spectral data from remote sensing sensors. © 2008 American Society for Photogrammetry and Remote Sensing.

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