Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Simulation model for natural gas transmission pipeline network system

This paper focuses on developing a simulation model for the analysis of transmission pipeline network system (TPNS) with detailed characteristics of compressor stations. Compressor station is the key element in the TPNS since it provides energy to keep the gas moving. The simulation model is used to create a system that simulates TPNS with different configurations to get pressure and flow parameters. The mathematical formulations for the TPNS simulation were derived from the principles of flow of fluid through pipe, mass balance and compressor characteristics. In order to determine the unknown pressure and flow parameters, a visual C++ code was developed based on Newton–Raphson solution technique. Using the parameters obtained, the model evaluates the energy consumption for various configurations in order to guide for the selection of optimal TPNS. Results from the evaluations of the model with the existing TPNS and comparison with the existing approaches showed that the developed simulation model enabled to determine the operational parameters with less than 10 iterations. Hence, the simulation model could assist in decisions regarding the design and operations of the TPNS.     

Role-based access to facilities lifecycle information on RFID tags

Radio Frequency Identification (RFID) based systems have been used in different applications in the Architecture, Engineering, Construction, Owner and Operator (AECOO) industry. Applications are mainly designed for specific lifecycle stage of the facility and serve the needs of only one of the stakeholders. This would increase the cost and the labor for adding and removing tags and eliminate the chance of using shared resources. In this paper, the usage of tags permanently attached to components is proposed where the memory of the tags is used during the lifecycle by different stakeholders for data storage and handover. A Building Information Model (BIM) database is used for tackling the interoperability issues allowing different users to access and share the data. To securely and efficiently store data on RFID tags in ifcXML format, multi-level encryption together with role-based access control is applied on the data stored on RFID tags. Each user is assigned a certain role and can only access the part of data for which he has authorization according to a predefined role and the Access Control Policy. To explore the technical feasibility of the proposed approach, a case study considering both facilities management and emergency management has been implemented and tested at Concordia University.     

Slippage control in soft finger grasping and manipulation

Handling objects with robotic soft fingers without considering the odds of slippage are not realistic. Grasping and manipulation algorithms have to be tested under such conditions for evaluating their robustness. In this paper, a dynamic analysis of rigid object manipulation with slippage control is studied using a two-link finger with soft hemispherical tip. Dependency on contact forces applied by a soft finger while grasping a rigid object is examined experimentally. A power-law model combined with a linear viscous damper is used to model the elastic behavior and damping effect of the soft tip, respectively. In order to obtain precise dynamic equations governing the system, two second-order differential equations with variable coefficients have been designed to describe the different possible states of the contact forces accordingly. A controller is designed based on the rigid fingertip model using the concept of feedback linearization for each phase of the system dynamics. Numerical simulations are used to evaluate the performance of the controller. The results reveal that the designed controller shows acceptable performance for both soft and rigid finger manipulation in reducing and canceling slippage. Furthermore, simulations indicate that the applied force in the soft finger manipulation is considerably less than the rigid “one.”.     

A learning-based visual saliency prediction model for stereoscopic 3D video (LBVS-3D)

Saliency prediction models provide a probabilistic map of relative likelihood of an image or video region to attract the attention of the human visual system. Over the past decade, many computational saliency prediction models have been proposed for 2D images and videos. Considering that the human visual system has evolved in a natural 3D environment, it is only natural to want to design visual attention models for 3D content. Existing monocular saliency models are not able to accurately predict the attentive regions when applied to 3D image/video content, as they do not incorporate depth information. This paper explores stereoscopic video saliency prediction by exploiting both low-level attributes such as brightness, color, texture, orientation, motion, and depth, as well as high-level cues such as face, person, vehicle, animal, text, and horizon. Our model starts with a rough segmentation and quantifies several intuitive observations such as the effects of visual discomfort level, depth abruptness, motion acceleration, elements of surprise, size and compactness of the salient regions, and emphasizing only a few salient objects in a scene. A new fovea-based model of spatial distance between the image regions is adopted for considering local and global feature calculations. To efficiently fuse the conspicuity maps generated by our method to one single saliency map that is highly correlated with the eye-fixation data, a random forest based algorithm is utilized. The performance of the proposed saliency model is evaluated against the results of an eye-tracking experiment, which involved 24 subjects and an in-house database of 61 captured stereoscopic videos. Our stereo video database as well as the eye-tracking data are publicly available along with this paper. Experiment results show that the proposed saliency prediction method achieves competitive performance compared to the state-of-the-art approaches.

     

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.     

Identification and classification of ERP critical failure factors in Iranian industries

Implementation of Enterprise Resource Planning systems (ERPs) is a complex and costly process, which usually results in serious failures. The majority of previous studies and research projects have been conducted in identifying ERP Critical Success Factors (CSFs) rather than Critical Failure Factors (CFFs). On the other hand, most studies have been devoted to developed countries, while in developing countries, many companies have moved towards using such systems. The majority of IT/IS management standards and guidelines have been developed by technologically-leading countries. But developing countries, which mostly confront with especial challenges, have a different condition from the implicit assumptions of leading countries. Iran is one of these countries that many of its ERP implementation projects led to failure. There is no comprehensive study that identifies ERP CFFs in Iranian industries. The main purpose of this study is to identify such factors and classify them to help other industries, consultants and implementers to prevent failures in the implementation of ERP projects. So, at first, with the semi structured interviews and literature reviews, 47 failure factors were identified. After that a questionnaire developed and sent to ERP project team members in Iranian industries that failed in their ERP projects. Robust Exploratory Factor Analysis (EFA) has been used for data analysis, which finally classified critical failure factors in seven groups named as vendor and consultant, human resources, managerial, project management, processes, organizational and technical. The results of this study have provided a very useful reference for scholars and managers to identify the relevant issues of ERP projects failure in developing countries.     

Heat transfer in a turbulent jet impinging on a moving plate considering high plate-to-jet velocity ratios

In this paper, heat transfer characteristics of a turbulent slot jet impinging orthogonally on an isothermal moving hot plate is studied numerically. The governing equations were discretized using the finite volume method and the υ ² — f turbulence model was employed for turbulence modeling. The effect of the jet Reynolds number and the plate-to-jet velocity ratio (R) on the Nusselt were investigated. Despite of most previous studies, which have been restricted to R≤2, in the present research higher values of R, also were considered (0≤R≤6). Range of studied jet Reynolds number was between 3000 and 60000. The results indicate that at a fixed plate-to-jet velocity ratio increment of the Reynolds number leads to the enhancement of the average Nusselt number. For each Reynolds number, the average Nusselt number reduces with increasing the plate-to-jet velocity ratio until it becomes minimum at R = 1.25. For R>1.25 trend changes so that these parameters increase. In addition, it was found that only for R>2.5 the average Nusselt number is improved due to the plate motion in comparison with the stationary jet. The results are validated against available experimental data, showing good agreement.