Crowd Modeling and Simulation for Safer Building Design

Crowd modeling and simulation are very important in the investigation and study of the dynamics of a crowd. They can be used not only to understand the behavior of a crowd in different environments, but also in risk assessment of spaces and in designing spaces that are safer for crowds, especially during emergency evacuations. This paper provides an overview of the use of the crowd simulation model for three main purposes; (1) as a modeling tool to simulate behavior of a crowd in different environments, (2) as a risk assessment tool to assess the risk posed in the environment, and (3) as an optimization tool to optimize the design of a building or space so as to ensure safer crowd movement and evacuation. Result shows that a simulation using the magnetic force model with a pathfinding feature provides a realistic crowd simulation and the use of ABC optimization can reduce evacuation time and improve evacuation comfort. This paper is expected to provide readers with a clearer idea on how crowd models are used in ensuring safer building planning and design

Wavelet neural network-based narma-l2 internal model control utilizing micro-artificial immune techniques to control nonlinear systems

This paper presents an intelligent control strategy based on internal model control (IMC) to control nonlinear systems. In particular, a wavelet neural network (WNN)-based nonlinear autoregressive moving average (NARMA-L2) network is used to acquire the forward dynamics of the controlled system. Subsequently, the control law can be directly derived. In this approach, a single NARMA-L2 with only one training phase is required. Hence, unlike other related works, this design approach does not require an additional training phase to find the model inversion. In the literature, gradient descent methods are the most widely applied training techniques for the neural network-based IMC. However, these methods are characterized by the slow convergence speed and the tendency to get trapped at local minima. To avoid these limitations, the newly developed modified micro-artificial immune system (modified Micro-AIS) is employed in this work to train the NARMA-L2. The simulation results have demonstrated the effectiveness of the proposed approach in terms of accurate control and robustness against external disturbances. In addition, a comparative study has shown the superiority of the WNN over the multilayer perceptron and the radial basis function based IMC. Moreover, compared with the genetic algorithm, the modified Micro-AIS has achieved better results as the training method in the IMC structure

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

Comprehensive pineapple segmentation techniques with intelligent convolutional neural network

This paper proposes an intelligent segmentation technique for pineapple fruit using Convolutional Neural Network (CNN). Cascade Object Detector (COD) method is used to detect the position of the pineapple from the captured image by returning the bounding box around the detecting pineapple. Image background such as ground, sky and other unwanted objects have been removed using Hue value, Adaptive Red and Blue Chromatic Map (ARB) and Normalized Difference Index (NDI) methods. However, the ARB and NDI methods are still producing misclassified error and the edge is not really smooth. In this case Template Matching Method (TMM) has been implemented for image enhancement process. Finally, an intelligent CNN is developed as a decision maker to select the best segmentation image ouput from ARB and NDI. The results obtained show that the proposed intelligent method has successfully verified the fruit from the background with high accuracy as compared to the conventional method

Image analysis techniques for ripeness detection of palm oil fresh fruit bunches

Being one of the biggest producers and exporters of palm oil and palm oil products, Malaysia has an important role to play in fulfilling the growing global need for oils and fats sustainably. Quality is an important factor that ensuring palm oil industries fulfill the demands of palm oil product. There has significant relationship between the quality of the palm oil fruits and the content of its oil. Ripe FFB gives more oil content, while unripe FFB give the least content. Overripe FFB shows that the content of oil is deteriorates. There have 4 classes of ripeness stages involves in this paper which are ripe, unripe, underipe and overripe. The proposed approach in this paper uses color features and bag of visual word for classifying oil palm fruit ripeness stages. Experiments conducted in this paper consisted of smartphone camera for image acquisition, python and matlab software for image pre processing and Support Vector Machine for classification. A total of 400 images is taken in a few plant in north Malaysia. Experiments involved on a dataset of 360 images for training for four classes and 40 images for testing. The average accuracy for the 4 classes of the FFB by color features is 57% while the accuracy for ripeness classification by using bag of visual word is 70%

Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization

In energy management system (EMS), the scheduling of air-conditioning (AC) system has been shown to reduce considerable amount of its power consumption with relatively low implementation cost. However, most scheduling methods lack a systematic approach to ensuring optimal power consumption reduction and comfort experienced by occupants. The main contribution of this paper is a new optimized AC scheduling approach that focuses on indoor thermal comfort using a new multi-objective optimization algorithm, called the improved global particle swarm optimization (IGPSO), which able to find better optimal solutions faster than its original version, the global particle swarm optimization (GPSO) algorithm. IGPSO is used to model the building characteristics and to find optimum indoor temperature values for the room/building. The proposed technique is based on predicted mean vote (PMV) comfort index that is able to reduce AC power consumption while maintaining indoor comfort throughout its operation. The schedule is set in advance by making use of weather forecast and the estimation of building characteristic parameters. This technique can be implemented on existing buildings with existing HVAC systems with minimal modifications to the HVAC infrastructure. Experimental results show that the proposed method is able to provide good PMV while consuming less power compared to the commonly used extended pre-cooling technique

Modification of physical force approach for simulating agent movement with collective behavior

Crowd modelling is a simulation study to know how crowd will behave in the environment. This simulation will contribute general knowledge and insight especially for safety engineers and architectural designers in assessing safety of crowd movement in buildings. There are many existing crowd models. However, these models neglect the details of agent characteristics and intelligence on how the agent will behave in the real environment. Therefore, in this study, the aim is to present heterogeneous agent characteristics and to include intelligence in the model in order to produce collective types of agent behaviour by modify the existing physical force approach

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

Yaw stability improvement for four-wheel active steering vehicle using sliding mode control

Active steering control is one of the approach that can be used to improve the vehicle's lateral and yaw stability. By combining active front steering and active rear steering control, the vehicle's handling and stability can be improved via four wheel active steering (4WAS) control. In this paper, a robust control algorithm of sliding mode control is designed for 4WAS vehicle. Single track 2 d.o.f linear model is utilized for controller design and simulation purpose. Simulation for 4WAS and front steering (AFS) is carried out in Simulink for step steer and double lane change maneuver to verify the effectiveness of the proposed control system. The result shows that the 4WAS perform better than the AFS in tracking the desired response trajectory

Adaptive feature selection for denial of services (DoS) attack

Adaptive detection is the learning ability to detect any changes in patterns in intrusion detection systems. In this paper, we propose combining two techniques in feature selection algorithm, namely consistency subset evaluation (CSE) and DDoS characteristic features (DCF) to identify and select the most important and relevant features related DDoS attacks. The proposed technique is trained and tested using the NSL-KDD 2009 dataset and compared with the traditional features selection method such as Information Gain, Gain Ratio, Chi-squared and Correlated features selection (CFS). The result shows that the combined CSE with DCF model overcomes the drawback of traditional feature selection technique such as avoid over-fitting, long training time and improved efficiency of detections. The adaptive model based on this technique can reduce computational complexity to analyze the data when attack occurs