듀얼 스트림 CNN-LSTM 아키텍처를 사용한 태양광 발전 예측

Zulfiqar Ahmad Khan,Noman Khan,Su Min Lee,Sang Il Yoon,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.05

The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power challenges the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate prediction for power generation is important to provide balanced electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual stream Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to learn spatial patterns using CNN and temporal features via the LSTM network. These features are then fused via a concatenation layer and then feed forward to Dense layers for optimal features selection and future solar power prediction. The performance of the proposed model is evaluated on benchmark datasets and achieved a new state-of-the-art on these datasets.

Preliminary pharmacognostical and phytochemical evaluation of Stachys tibetica Vatke

Zulfiqar Ali Bhat,Dinesh Kumar,Vijender Kumar,Ishtaq Ahmad Chashoo,Nisar Ahmad Khan,Irfat Ara,Mohammad Yassin Shah 셀메드 세포교정의약학회 2012 TANG Vol.2 No.1

Stachys tibetica Vatke (Lamiaceae) is an important medicinal plant in the folk medicine of Ladakh, India and Tibet for the treatment of various mental disorders. Infusion and decoction of the whole plant is used as a cup of tea for a severe fever, headaches and to relieve tension. The recent study is aimed to evaluate the preliminary pharmacognostical and phytochemical nature of Stachys tibetica Vatke. The whole plant material was subjected to successive soxhlet extraction with petroleum ether (40 - 60C), chloroform, ethyl acetate, methanol and finally decocted with water to get the respective extracts. The fluorescence characteristics of the powdered materials were analysed under ultraviolet light and ordinary light. Different physicochemical parameters such as ash value, extractive value, foaming index, pH values, loss on drying and determination of foreign matter were carried out as per WHO guidelines. The total fat, flavonoid, saponin and volatile contents were also determined. Macroscopical studies revealed the authentication of the plant drug. Physicochemical parameters helped to standardize the plant material while preliminary qualitative chemical tests of different extracts showed the presence of Glycosides, Carbohydrates, Phytosterols/triterpenoids, Saponins, Fixed oils, Fats and phenols/tannins. Quantification of the total flavonoids and saponins and contents were determined as 54.66 ± 0.58 mg/g and 75.42 ± 0.48 mg/kg respectively, while the volatile and fat contents were 6.5% and 0.7% respectively. Results may lay the foundation for the standardization of the drug and discovery of new molecules from S. tibetica for the treatment of various diseases.

항공 장면에서의 효율적인 차량 감지

Habib Khan,Zulfiqar Ahmad Khan,Waseem Ullah,Min Jee Kim,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.06

Accurate detection of small targets in aerial images is crucial but challenging due to the limited computational resources of UAVs. This paper presents an efficient approach based on YOLO-V5S for detecting and classifying distant vehicles in aerial scenes. Extensive ablation study is conducted to find the optimal YOLO architecture. The proposed method is efficient and effective, making it applicable for real-time deployment. A dataset of 1000 annotated images are developed to validate the proposed method's effectiveness. The proposed network outperforms existing state-of-the-art methods in accuracy, speed, and resource efficiency, making it a promising solution for aerial vision-based applications.

Assessment of antibacterial cellulose nanocomposites for water permeability and salt rejection

Sher Bahadar Khan,Khalid A. Alamry,Elham N. Bifari,Abdullah M. Asiri,Muhammad Yasir,Lassaad Gzara,Rehan Zulfiqar Ahmad 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.24 No.-

Anti-bacterial nanocomposites (NC1–NC4) based on cellulose acetate were prepared by dispersing ZnO nanofillers in the cellulose acetate matrix. Anti-bacterial nanocomposites were structurally and morphological examined by XRD (X-ray powder diffraction), FESEM (field emission scanning electron microscopy), and FT-IR (Fourier transform infrared) spectroscopy. All the spectroscopic techniques suggested that nanocomposites are successfully synthesized. All the nanocomposites showed antibacterial activity which increased as a function of zinc oxide. Further the selectivity of anti-bacterial nanocomposites was investigated toward different metal ions, including Zn2+, Cd2+, Pb2+, Mn2+, Ni2+, Fe2+, Al3+, Sb3+, and Sr3+. The selectivity data suggests that nanocomposites are more selective toward Fe2+. NC1 displayed highest uptake aptitude for Fe2+ with highest distribution coefficient of 7549.123 mL g1. Therefore, NC1 was subjected to water permeability to explore the role of antibacterial nanocomposite as membrane for water purification. The results suggest that these materials are possibly appropriate for water treatments.

PV-ANet: Attention-Based Network for Short-term Photovoltaic Power Forecasting

Muhammad Munsif,Habib Khan,Zulfiqar Ahmad Khan,Altaf Hussain,Fath U Min Ullah,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10

Nowadays, renewable energy resources such as Photovoltaic (PV) is one of the convenient ways to integrate it into the distributed grid to fulfill the huge energy demands without burning costly and pollutant fossil fuels. Researchers have been contributing from various aspects to develop accurate PV-power forecasting methods however further improvements are needed for an effective power management system. Therefore, in this work, we propose an attention-based deep learning (DL) model (PV-ANet) for short-term PV-power forecasting. The proposed system mainly consists of three modules. First, data from an actual PV power plant is acquired and preprocessed to remove outliers and normalized for efficient processing. Next, the PV-ANet model is developed, which is consisting of an encoder and decoder modules. The encoder encodes the input attributes via stack conventional and attention layer. While the decoder part contains the normalization and series of the dense layers to expends the encoded features into optimal features and generate one hour ahead forecast. Finally, the proposed model is evaluated via standard error metrics including MSE, MAE, and RMSE and achieved the lowest errors rates compared to state-of-the-art methods.

이상행동 및 행동 인식 모델 학습 및 테스트를 위한 시스템 UI 설계에 대한 연구

이수민,권찬민,Tanveer Hussain,Samee Ullah Khan,Waseem Ullah,Noman Khan,Zulfiqar Ahmad Khan,이미영,백성욱 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.05

인공지능을 활용한 사업이 활발히 진행되면서 범죄 예방 및 안전분야와 관련하여 이상행동 및 행동 인식에 대한 연구와 관심이 높아지고 있다. 하지만 딥러닝 등 인공지능 모델을 생성하는 것은 전문 지식이 없는 경우 많은 어려움이 따른다. 본 논문에서는 사용자가 편리하게 딥러닝 모델을 생성할 수 있도록 데이터셋을 제공하고 이상행동 및 행동 인식 기술을 API화하여 인터페이스에서 호출하는 방식을 사용하는 사용자 친화적인 모델 학습 및 테스트를 위한 시스템 UI를 제안하였다. 본 논문에서 제안한 시스템은 딥러닝에 대한 사전 지식이 없는 사용자가 편리하게 딥러닝 모델을 생성할 수 있을 것으로 기대된다.

Use of Smoke-less Tobacco Amongst the Staff of Tertiary Care Hospitals in the Largest City of Pakistan

Valliani, Arif,Ahmed, Bilawal,Nanji, Kashmira,Valliani, Salimah,Zulfiqar, Beenish,Fakih, Misbah,Mehdi, Mehwish,Khan, Anam,Sheikh, Sana Arshad,Fatima, Nida,Ahmad, Sobia,Farah, Fariya,Saleem, Shaheera,A Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.5

Background: Use of smoke-less tobacco (SLT) is very common in South and South-East Asian countries. It is significantly associated with various types of cancers. The objectives of this study were to assess the proportion of hospital staff that use SLT, and to identify the factors associated with its use and their practices. Methods: In a cross-sectional study, 560 staff of two tertiary care hospitals were interviewed in the year 2009. Nurses, ward boys and technicians were counted as a paramedic staff while drivers, peons, security guards and housekeeping staff were labeled as non-paramedic staff. SLT use was considered as usage of any of the following: betel quid (paan) with or without tobacco, betel nuts with or without tobacco (gutkha) and snuff (naswar). Results: About half (48.6%) of the hospital staff were using at least one type of SLT. Factors found to be statistically significant with SLT were being a male (OR=2.5; 95% CI=1.8-3.7); having no/fewer years of education (OR=1.7; 95% CI=1.2-2.4) and working as non-paramedic staff (OR=2.6; 95% CI=1.8-3.8). Majority of SLT users were using it on regular basis, for > 5 years and keeping the tobacco products in the oral cavity for >30 minutes. About half of the users started due to peer pressure and had tried to quit this habit but failed. Conclusion: In this study, about half of the study participants were using SLT in different forms. We suggest educational and behavioral interventions for control of SLT usage.