Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

About: Ghulam Ishaq Khan Institute of Engineering Sciences and Technology is a education organization based out in Topi, Pakistan. It is known for research contribution in the topics: Quantum efficiency & Diode. The organization has 618 authors who have published 940 publications receiving 10674 citations.

Electric properties of flexible rubber-based CNT/CNT-OD/Al cells fabricated by rubbing-in technology

Abstract: Flexible rubber based CNT/CNT-OD/Al cells were designed, fabricated and tested for multi-sensing and filter applications. Rubbing-in technology was used to fabricate the cells containing 12–17 μm thick active layers of CNT-OD (50:50 wt%). I–V characteristics of the cells were measured and the nonlinearity coefficient (β) was found in the interval of 1.8 to 2.3. The cells were tested for pressure, displacement and temperature sensing in the range of 0–33 gf/cm2, 0–250 μm and 21–67 °C, respectively. Sensing mechanism was based on resistance/impedance change. The cells showed sensitivities up to −424.2 Ωcm2/gf, −40.0 Ω/μm and −402 Ω/°C for pressure, displacement and temperature, respectively. The cells’ cutoff frequency was equal to 20 kHz. The fabricated cells potentially can be used as displacement, pressure, temperature sensors and frequency low pass filter as well.

Comprehensive dynamic modeling, simulation, and validation for an industrial boiler incident investigation

Abstract: Industrial boilers simulation can serve number of purposes like training, optimization, HAZOP studies, and incident investigation. Simulation including trip logics, interlockings, safety devices, start‐up and shutdown procedure is presented in this work. Simulation is directly accomplished in dynamic mode to benefit from the holdup approach to avoid anomalies because of recycling. The industrial DCS/PLC diagrams are replicated to imitate the boiler‐integrated behavior. Trip logics, start‐up and shutdown procedures are programmed using event scheduler. The inbuilt pressure relief module validates the simulated PSVs and rupture disc capacities. The model is then verified at three stages including ‘steady state’ achievement in dynamic mode, controller validation by start‐up and shut down procedures and finally by direct industrial data under deviant conditions. Where an unfortunate event is a concern, it also provides an opportunity to ponder and to evolve at various levels. Operational simulation of any such incident may help in getting a deep insight and thus to take accurate countermeasures. One such attempt is done in this work. A boiler accident has been reported. The complete response of the controllers and interlockings/trip system is imitated. The obtained results were compared with the chain of events that took place. © 2019 American Institute of Chemical Engineers Process Saf Prog: e12040 2019

On identification of big-five personality traits through choice of images in a real-world setting

Abstract: Studying multiple human personality traits utilizing modern Artificial Intelligence (AI) techniques has recently gained popularity. Past studies regarding human personality assessment have used paper pencil methods, self-reports, and questionnaires. Due to the proliferation of various technologies, advancement in the Internet and use of social media networks, the concept of utilizing images, text, and videos to model human personality is gaining popularity. This work utilizes an AI-based framework to predict human personality with respect to the Big-Five model based on the choice of images made in a real-world setting. For this, the current proposal uses a dataset of real-life images that are directly/indirectly related to positive and negative sides of each of the Big-Five personality traits. Using different image seeking tasks, a data is collected from 77 participants through a custom-built tool. For creating the ground truth (about the personality of these participants), the IPIP-NEO-120 (International Personality Item Pool Representation of the NEO) personality test is taken by the participants. Results recorded are later used to correlate with the trait percentile extracted through image selection tasks. Pearson correlation coefficient is used for computing the correlation between the personality profiles of the participants. The correlation test reveals that 82% of the results are positively correlated with a p-value of 0.02. Using this data, three AI classifiers, namely, Support Vector Machine (SVM), k-nearest neighbors (k-NN), and Artificial Neural Networks (ANN) are trained for predicting the personality traits of the participants. Initially, these classifiers are trained to categorize a person being high, average, low or very low in a personality trait. Where, the maximum average accuracy of 83% is achieved by SVM for predicting agreeableness utilizing the polynomial kernel having degree six. Later, these classifiers are trained to predict the dominant personality trait, for which five class labels (i.e., O, C, E, A, and N) are assigned based on the highest percentile among all five traits. Where, SVM outperforms k-NN and ANN with an average accuracy of 70%. The results reveal that different aspects of human personality can be predicted with sufficient accuracy using an individual’s choice of images in a real-world setting.

Processing and microstructure of alumina-based composites

Abstract: A study of powder structure and its effect on the sintering tendency of certain alumina-based ceramic systems, that is, Al2O3-SiO2 and Al2O3-ZrO2, was carried out to improve their mechanical strength and fracture toughness. The compacting behavior and the sintering characteristics were optimized through control of various parameters such as composition, compaction pressure, sintering temperature, and time. Best densification was obtained for mixtures prepared using very fine and deagglomerated alumina powders.