Ultracapacitors for port crane applications: Sizing and techno-economic analysis

The use of energy storage with high power density and fast response time at container terminals (CTs) with a power demand of tens of megawatts is one of the most critical factors for peak reduction and economic benefits. Peak shaving can balance the load demand and facilitate the participation of small power units in generation based on renewable energies. Therefore, in this paper, the economic efficiency of peak demand reduction in ship to shore (STS) cranes based on the ultracapacitor (UC) energy storage sizing has been investigated. The results show the UC energy storage significantly reduce the peak demand, increasing the load factor, load leveling, and most importantly, an outstanding reduction in power and energy cost. In fact, the suggested approach is the start point to improve reliability and reduce peak demand energy consumption

Housing Instability in the Lafayette Community and Beyond

This paper addresses the issue of housing instability low-income families face and the ways the community can help. The issue is prevalent in the Lafayette community and beyond. Habitat for Humanity is a nonprofit organization working to raise awareness of housing instability in our community, specifically through the ReStore of Lafayette. The ReStore is run solely on the community’s donations and volunteer work. All proceeds from the store go to building affordable houses for low-income families. My objectives in the research I conducted were to gain a better understanding of how the ReStore goes about eliminating housing instability in the United States and other countries. To do this, I posed this question: How can my volunteering hours at the ReStore help improve the marketing/advertising of their resources and address the housing instability many people face in the Lafayette community and beyond? Through primary and secondary research, I was able to come up with an answer that would be feasible for Purdue students and the Lafayette community. Journaling my thoughts and observations after each volunteer session helped me better reflect on my work and contribution to the housing instability issue of Lafayette. The activity theory is a concept about the flow of community service that I drew upon. This theory helped me visualize the interconnectedness between the community and the Restore. In my results section, I was informed about the ReStore’s current advertising methods and customer demographics. Being an active volunteer is the first step to effective community service. The more volunteers there are, the more awareness on the issue. Moving forward, I feel that more individuals should be aware of housing instability from a young age. More widespread awareness of the housing instability many people face in our community will benefit everyone in the long run

A Study of Semi­ridged and Non­Linear Behaviour of Nailed Joints in Timber Portal Frames

SYNOPSIS In this paper a study is made of the semi-rigid and non-linear behaviour of nailed knee joints in timber frames. A numerical analysis is applied to the non-linear deformation of the joints. The solution is derived using the stiffness method of analysis in which the rotation of each nailed joint is modelled by a series of piece-wise linear relationships, based on the load-deformation characteristics of nailed joints laterally loaded in single shear. The stiffness matrix is corrected at each step, to allow for joint flexibility; and the short-term non-linear deformation of frames, up to ultimate load, is calculated. Experimental verification is made using knee joint specimens and 6 m span timber portals with nailed plywood knee joints. The test results confirm the applicability of the analysis

Reliable and High-Performance Hardware Architectures for the Advanced Encryption Standard/Galois Counter Mode

The high level of security and the fast hardware and software implementations of the Advanced Encryption Standard (AES) have made it the first choice for many critical applications. Since its acceptance as the adopted symmetric-key algorithm, the AES has been utilized in various security-constrained applications, many of which are power and resource constrained and require reliable and efficient hardware implementations. In this thesis, first, we investigate the AES algorithm from the concurrent fault detection point of view. We note that in addition to the efficiency requirements of the AES, it must be reliable against transient and permanent internal faults or malicious faults aiming at revealing the secret key. This reliability analysis and proposing efficient and effective fault detection schemes are essential because fault attacks have become a serious concern in cryptographic applications. Therefore, we propose, design, and implement various novel concurrent fault detection schemes for different AES hardware architectures. These include different structure-dependent and independent approaches for detecting single and multiple stuck-at faults using single and multi-bit signatures. The recently standardized authentication mode of the AES, i.e., Galois/Counter Mode (GCM), is also considered in this thesis. We propose efficient architectures for the AES-GCM algorithm. In this regard, we investigate the AES algorithm and we propose low-complexity and low-power hardware implementations for it, emphasizing on its nonlinear transformation, i.e., SubByes (S-boxes). We present new formulations for this transformation and through exhaustive hardware implementations, we show that the proposed architectures outperform their counterparts in terms of efficiency. Moreover, we present parallel, high-performance new schemes for the hardware implementations of the GCM to improve its throughput and reduce its latency. The performance of the proposed efficient architectures for the AES-GCM and their fault detection approaches are benchmarked using application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) hardware platforms. Our comparison results show that the proposed hardware architectures outperform their existing counterparts in terms of efficiency and fault detection capability

Mining High Utility Patterns Over Data Streams

Mining useful patterns from sequential data is a challenging topic in data mining. An important task for mining sequential data is sequential pattern mining, which discovers sequences of itemsets that frequently appear in a sequence database. In sequential pattern mining, the selection of sequences is generally based on the frequency/support framework. However, most of the patterns returned by sequential pattern mining may not be informative enough to business people and are not particularly related to a business objective. In view of this, high utility sequential pattern (HUSP) mining has emerged as a novel research topic in data mining recently. The main objective of HUSP mining is to extract valuable and useful sequential patterns from data by considering the utility of a pattern that captures a business objective (e.g., profit, users interest). In HUSP mining, the goal is to find sequences whose utility in the database is no less than a user-specified minimum utility threshold. Nowadays, many applications generate a huge volume of data in the form of data streams. A number of studies have been conducted on mining HUSPs, but they are mainly intended for non-streaming data and thus do not take data stream characteristics into consideration. Mining HUSP from such data poses many challenges. First, it is infeasible to keep all streaming data in the memory due to the high volume of data accumulated over time. Second, mining algorithms need to process the arriving data in real time with one scan of data. Third, depending on the minimum utility threshold value, the number of patterns returned by a HUSP mining algorithm can be large and overwhelms the user. In general, it is hard for the user to determine the value for the threshold. Thus, algorithms that can find the most valuable patterns (i.e., top-k high utility patterns) are more desirable. Mining the most valuable patterns is interesting in both static data and data streams. To address these research limitations and challenges, this dissertation proposes techniques and algorithms for mining high utility sequential patterns over data streams. We work on mining HUSPs over both a long portion of a data stream and a short period of time. We also work on how to efficiently identify the most significant high utility patterns (namely, the top-k high utility patterns) over data streams. In the first part, we explore a fundamental problem that is how the limited memory space can be well utilized to produce high quality HUSPs over the entire data stream. An approximation algorithm, called MAHUSP, is designed which employs memory adaptive mechanisms to use a bounded portion of memory, to efficiently discover HUSPs over the entire data streams. The second part of the dissertation presents a new sliding window-based algorithm to discover recent high utility sequential patterns over data streams. A novel data structure named HUSP-Tree is proposed to maintain the essential information for mining recenT HUSPs. An efficient and single-pass algorithm named HUSP-Stream is proposed to generate recent HUSPs from HUSP-Tree. The third part addresses the problem of top-k high utility pattern mining over data streams. Two novel methods, named T-HUDS and T-HUSP, for finding top-k high utility patterns over a data stream are proposed. T-HUDS discovers top-k high utility itemsets and T-HUSP discovers top-k high utility sequential patterns over a data stream. T-HUDS is based on a compressed tree structure, called HUDS-Tree, that can be used to efficiently find potential top-k high utility itemsets over data streams. T-HUSP incrementally maintains the content of top-k HUSPs in a data stream in a summary data structure, named TKList, and discovers top-k HUSPs efficiently. All of the algorithms are evaluated using both synthetic and real datasets. The performances, including the running time, memory consumption, precision, recall and Fmeasure, are compared. In order to show the effectiveness and efficiency of the proposed methods in reallife applications, the fourth part of this dissertation presents applications of one of the proposed methods (i.e., MAHUSP) to extract meaningful patterns from a real web clickstream dataset and a real biosequence dataset. The utility-based sequential patterns are compared with the patterns in the frequency/support framework. The results show that high utility sequential pattern mining provides meaningful patterns in real-life applications