Search results for: Kaiwen Sun
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Search results for: Kaiwen Sun

3 Enhanced Optical and Electrical Properties of P-Type AgBiS₂ Energy Harvesting Materials as an Absorber of Solar Cell by Copper Doping

Authors: Yasaman Tabari-Saadi, Kaiwen Sun, Jialiang Huang, Martin Green, Xiaojing Hao

Abstract:

Optical and electrical properties of p-type AgBiS₂ absorber material have been improved by copper doping on silver sites. X-Ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) analysis suggest that complete solid solutions of Ag₁₋ₓCuₓBiS₂ thin film have been formed. The carrier concentration of pure AgBiS₂ thin film deposited by the chemical process is 4.5*E+14 cm⁻³, and copper doping leads to the improved carrier concentration despite the semiconductor AgBiS₂ remains p-type semiconductor. Copper doping directly changed the absorption coefficient and increased the optical band gap (~1.5eV), which makes it a promising absorber for thin-film solar cell applications.

Keywords: copper doped, AgBiS₂, thin-film solar cell, carrier concentration, p-type semiconductor

Procedia PDF Downloads 84
2 Investigation of Wood Chips as Internal Carbon Source Supporting Denitrification Process in Domestic Wastewater Treatment

Authors: Ruth Lorivi, Jianzheng Li, John J. Ambuchi, Kaiwen Deng

Abstract:

Nitrogen removal from wastewater is accomplished by nitrification and denitrification processes. Successful denitrification requires carbon, therefore, if placed after biochemical oxygen demand (BOD) and nitrification process, a carbon source has to be re-introduced into the water. To avoid adding a carbon source, denitrification is usually placed before BOD and nitrification processes. This process however involves recycling the nitrified effluent. In this study wood chips were used as internal carbon source which enabled placement of denitrification after BOD and nitrification process without effluent recycling. To investigate the efficiency of a wood packed aerobic-anaerobic baffled reactor on carbon and nutrients removal from domestic wastewater, a three compartment baffled reactor was presented. Each of the three compartments was packed with 329 g wood chips 1x1cm acting as an internal carbon source for denitrification. The proposed mode of operation was aerobic-anoxic-anaerobic (OAA) with no effluent recycling. The operating temperature, hydraulic retention time (HRT), dissolved oxygen (DO) and pH were 24 ± 2 , 24 h, less than 4 mg/L and 7 ± 1 respectively. The removal efficiencies of chemical oxygen demand (COD), ammonia nitrogen (NH4+-N) and total nitrogen (TN) attained was 99, 87 and 83% respectively. TN removal rate was limited by nitrification as 97% of ammonia converted into nitrate and nitrite was denitrified. These results show that application of wood chips in wastewater treatment processes is an efficient internal carbon source. 

Keywords: aerobic-anaerobic baffled reactor, denitrification, nitrification, wood chip

Procedia PDF Downloads 264
1 A Machine Learning Approach for Detecting and Locating Hardware Trojans

Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

Abstract:

The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

Procedia PDF Downloads 102