Kajian motivasi ekstrinsik di antara Pelajar Lepasan Sijil dan Diploma Politeknik Jabatan Kejuruteraan Awam KUiTTHO

Kajian ini dijalankan untuk menyelidiki pengaruh dorongan keluarga, cara pengajaran pensyarah, pengaruh rakan sebaya dan kemudahan infrastruktur terhadap motivasi ekstrinsik bagi pelajar tahun tiga dan tahun empat lepasan sijil dan diploma politeknik Jabatan Kejuruteraan Awain Kolej Universiti Teknologi Tun Hussein Onn. Sampel kajian ini beijumlah 87 orang bagi pelajar lepasan sijil politeknik dan 38 orang bagi lepasan diploma politeknik. Data kajian telah diperolehi melalui borang soal selidik dan telah dianalisis menggunakan perisian SPSS (Statical Package For Sciences). Hasil kajian telah dipersembahkan dalam bentuk jadual dan histohgrapi. Analisis kajian mendapati bahawa kedua-dua kumpulan setuju bahawa faktor-faktor di atas memberi kesan kepada motivasi ekstrinsik mereka. Dengan kata lain faktpr-faktor tersebut penting dalam membentuk pelajar mencapai kecemerlangan akademik

Kepentingan mata pelajaran kokurikulum di kalangan pelajar institusi pengajian tinggi: tinjauan ke atas pelajar tahun akhir Ijazah Sarjana Muda Kejuruteraan Elektrik di KUITTHO

Kajian ini dilakukan adalah untuk mengetahui kepentingan mata pelajaran kokurikulum kepada pelajar-pelajar di institusi pengajian tinggi. Di dalam kajian ini borang soal selidik telah digunakan bagi mendapatkan maklumat yang diperlukan Seramai 80 orang responden daripada pelajar tahun akhir ijazah saijana muda kejuruteraan elektrik KUiTTHO telah dipilih bagi menjalankan kajian ini. Analisis data telah dibuat dengan menggunakan kaedah Statistical Package for Social Science (SPSS) bagi mendapatkan nilai peratusan dan min. Hasil kajian telah menunjukkan 33.8% daripada responden melibatkan diri di dalam aktiviti kokurikulum adalah sebagai memenuhi syarat wajib yang telah ditetapkan oleh pihak KUiTTHO. Hasil kajian juga menunjukkan 71.3% daripada responden lebih tertarik kepada kegiatan kokurikulum berbentuk sukan dan rekreasi. Hasil kajian juga menunjukkan bahawa kebanyakan daripada responden mempunyai pandangan yang positif terhadap kepentingan melibatkan diri di dalam kegiatan kokurikulum. Namun begitu, diharapkan agar cadangan yang dikemukakan akan dapat meningkatkan lagi kesedaran di kalangan pelajar-pelajar IPT terhadap kepentingan melibatkan diri di dalam kegiatan kokurikulu

Development of an experimental test bench for an electronically control fuel injection system

Electronic fuel injection (EFI) system is a fuel delivery system that is controlled electronically with an electronic control unit (ECU) used in most modern vehicle’s engine. As the fuel injection runs on a vehicle engine, it is difficult to observe the overall behavior of the fuel injection system. A test bench for a 4-cylinder engine is generally developed to run the ECU without the real engine. The development of the test bench described in this paper includes the fabrication of the mechanical model of the test bench, the use of a signal generator for the input signals representing the various signals of an engine and the development of a computer control algorithm for the four-cylinder engine to provide optimum power and fuel efficiency for the engine. The input signal generation of the crankshaft signal and throttle position signal that are similar to the real signal provided by an engine is also discussed. The development of a cost-effective ECU that calculates the suitable amount of fuel to be delivered at correct timings and sequence is also explained. The important part of this paper is the control of the amount of time needed for the injectors to remain open to give the accurate amount fuel injected as well as to control the injection timing of a 4-cylinder engine sequence. The test bench can also be used for several experiments that require the measurement of fuel injected such as fuel injector performance test

Automotive real-time data acquisition using Wi-Fi connected embedded system

The advancement in embedded systems, which includes the mass deployment of internet-connected electronics, allows the concept of Internet of Things (IoT), to become a reality. This paper discusses one example of how an internet-connected embedded system is utilized in an automotive system. An Electronic Control Unit (ECU), which functions as a control unit in a fuel injection system, are equipped with Wi-Fi capability and installed on 110cc motorcycle. The ECU is connected to multiple sensors that is used by the ECU as part of control system, as well as giving raw data in real time to the server by using Wi-Fi as the communication medium. The server will accumulate data transmitted from ECU by using MQTT protocol, chosen due to its minimal data profile. The data can be visualized through web portal, or opened by any other web-enabled devices. The data collected may also be used later for any other purposes, such as On-Board Diagnostics (OBD) system, etc

Small engine load estimator for fuel injection system using two-stage neural network

Most motorcycles in developing countries use carburetor systems as fuel delivery method especially for models with cubic capacity of less than 350 cc. However, small gasoline carbureted engines suffer from low operating efficiency, high fuel consumption and high level of hazardous emissions. In recent years, Electronic Fuel Injection (EFI) technology has been applied to small engine motorcycles as well. EFI system has better fuel economy and can reduce harmful emissions by correctly calculating suitable amount of fuel to be injected into the combustion chamber. One way to achieve this is by accurately estimate the engine load by using the in-cylinder Air Mass Flow (AMF) rate of the engine. Most of the control schemes in modern system either approximate the AMF near the throttle plate using Mass Air Flow (MAF) sensor or in the intake manifold using Manifold Absolute Pressure (MAP) sensor. This work presents a more economical approach to estimate the AMF by using only the measurements of throttle position and engine speed, that is, without using the MAF sensor or the MAP sensor to estimate the AMF in intake manifold, resulting in lower implementation cost. The estimation is done via two-stage multilayer feed-forward neural network with combinations of Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results in 20 runs, the second variant of hybrid algorithm yields a better network performance with a mean squared error (MSE) of 1.8308 by estimating the AMF closely to the simulated AMF values compared to using the first variant of hybrid algorithm (MSE of 2.8906), LM (MSE of 8.0525), LM with BR (MSE of 3.5657) and PSO (MSE of 133.7900) alone. By using a valid experimental training data, the estimator network trained with the second variant of the hybrid algorithm showed the best performance, with MSE of 1.9863, among other algorithms when used in an actual small engine fuel injection system

Electronic control unit design for a retrofit fuel injection system of a 4-stroke 1-cylinder small engine

Most motorcycles in developing countries use carburetor systems as the fuel delivery method especially for models with the cubic capacity of less than 125cc. However, small gasoline fuelled engines operating using carburetor system suffer from low operating efficiency, waste of fuel and produce higher level of hazardous emissions to the environment. In this study, an electronic control unit (ECU) is designed and simulated for a retrofit fuel injection (FIS) system. The ECU is targeted to have a simple design, reliable and offers all of the necessary functions of the modern ECU. The simulation results shows that the designed ECU can determine the injection period as close to the proposed value and can drive the injector efficiently based on the generated PWM pulse

Manifold absolute pressure estimation using neural network with hybrid training algorithm

<div><p>In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.</p></div

Comparison between estimated and measured MAP in transient operation.

<p>Comparison between estimated and measured MAP in transient operation.</p

Variation of the network (MAP estimator) test MSE with the number of hidden neuron for four training algorithms.

<p>Variation of the network (MAP estimator) test MSE with the number of hidden neuron for four training algorithms.</p

Comparison of the MAP estimator output (LM) and actual MAP as a function of (a) throttle and (b) speed.

<p>Comparison of the MAP estimator output (LM) and actual MAP as a function of (a) throttle and (b) speed.</p