Semantic image retrieval using relevance feedback and transaction logs

Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible, and efficient means to retrieve images matching a user’s query are needed. Content-based Image Retrieval (CBIR) systems automatically extract image contents based on image features, i.e. color, texture, and shape. Relevance feedback methods are applied to CBIR to integrate users’ perceptions and reduce the gap between high-level image semantics and low-level image features. The precision of a CBIR system in retrieving semantically rich (complex) images is improved in this dissertation work by making advancements in three areas of a CBIR system: input, process, and output. The input of the system includes a mechanism that provides the user with required tools to build and modify her query through feedbacks. Users behavioral in CBIR environments are studied, and a new feedback methodology is presented to efficiently capture users’ image perceptions. The process element includes image learning and retrieval algorithms. A Long-term image retrieval algorithm (LTL), which learns image semantics from prior search results available in the system’s transaction history, is developed using Factor Analysis. Another algorithm, a short-term learner (STL) that captures user’s image perceptions based on image features and user’s feedbacks in the on-going transaction, is developed based on Linear Discriminant Analysis. Then, a mechanism is introduced to integrate these two algorithms to one retrieval procedure. Finally, a retrieval strategy that includes learning and searching phases is defined for arranging images in the output of the system. The developed relevance feedback methodology proved to reduce the effect of human subjectivity in providing feedbacks for complex images. Retrieval algorithms were applied to images with different degrees of complexity. LTL is efficient in extracting the semantics of complex images that have a history in the system. STL is suitable for query and images that can be effectively represented by their image features. Therefore, the performance of the system in retrieving images with visual and conceptual complexities was improved when both algorithms were applied simultaneously. Finally, the strategy of retrieval phases demonstrated promising results when the query complexity increases

Leveraging Sensory Data in Estimating Transformer Lifetime

Transformer lifetime assessments plays a vital role in reliable operation of power systems. In this paper, leveraging sensory data, an approach in estimating transformer lifetime is presented. The winding hottest-spot temperature, which is the pivotal driver that impacts transformer aging, is measured hourly via a temperature sensor, then transformer loss of life is calculated based on the IEEE Std. C57.91-2011. A Cumulative Moving Average (CMA) model is subsequently applied to the data stream of the transformer loss of life to provide hourly estimates until convergence. Numerical examples demonstrate the effectiveness of the proposed approach for the transformer lifetime estimation, and explores its efficiency and practical merits.Comment: 2017 North American Power Symposium (NAPS), Morgantown, WV, 17-19 Sep. 201

Multicriteria decision-making method for sustainable site location of post-disaster temporary housing in urban areas

Many people lose their homes around the world every year because of natural disasters, such as earthquakes, tsunamis, and hurricanes. In the aftermath of a natural disaster, the displaced people (DP) have to move to temporary housing (TH) and do not have the ability to choose the settlement dimensions, distributions, neighborhood, or other characteristics of their TH. Additionally, post-disaster settlement construction causes neighborhood changes, environmental degradation, and large-scale public expenditures. This paper presents a new model to support decision makers in choosing site locations for TH. The model is capable of determining the optimal site location based on the integration of economic, social, and environmental aspects into the whole life cycle of these houses. The integrated value model for sustainable assessment (MIVES), a multicriteria decision making (MCDM) model, is used to assess the sustainability of the aforementioned aspects, and MIVES includes the value function concept, which permits indicator homogenization by taking into account the satisfaction of the involved stakeholders.Peer ReviewedPostprint (author's final draft

Recent Multicast Routing Protocols in VANET: Classification and Comparison

الشبكة المخصصة للسيارات (VANET) صنفت باعتبارها واحدة من أهم فئات شبكات الجيل التالي التي طورت في السنوات الأخيرة بسرعة بالنسبة للمركبات وعمليات نقل الطرق. هذه الشبكه يمكن أن تساعد في تنفيذ مجموعة كبيرة من التطبيقات المتعلقة بالمركبات، اشارة المرور، ازدحام المرور، السائقين، الركاب، الإسعاف، الشرطة، سيارات الإطفاء وحتى المشاة. التوجيه هو المشكلة الأبرز في نقل المعلومات في الـ VANET وهناك العديد من وسائط النشر: البث الاحادي، البث المتعدد و البحث في منطقه جغرافيه معينه (geocast). في هذه المقاله سوف نركز فقط على الإرسال المتعدد الذي يشير إلى عملية إرسال معلومات من عقدة واحدة (تسمى المركبة المصدر) إلى مجموعة من العقد الموجودة في مواقع مختلفة (تسمى المركبات الهدف). والغرض من هذه المقالة هو دراسة بروتوكولات توجيه الإرسال المتعدد الموجودة في الـ VANET وإنتاج دراسه جيد عنها وتحديد مزايا وعيوب كل منها وكذلك تصنيفها إلى فئات مختلفة استنادا إلى بعض العوامل المؤثرة مثل نوعية الخدمة، مسار المركبة وما إلى ذلك. وبعد تحليل بروتوكولات التوجيه هذه وجدنا أن هناك حاجة ملحة لإنتاج بروتوكول توجيه متعدد الإرسال فعال لهذه الشبكة لتقليل استهلاك الموارد وتحسين الأداء العام.Vehicular Ad Hoc Network (VANET) classified as one of the most important classes of next generation networks that developed in recent years rapidly for vehicles and road transmissions. It can help in implementing a large set of applications related to vehicles, traffic light, traffic jam, drivers, passengers, ambulance, police, fire trucks and even pedestrians. Routing is the most prominent problem in the transmission of information in VANETs and there are many modes of dissemination: unicast, broadcast, multicast and geocast. In this paper, we will focus only on the multicast that is referring to a process of sending information from one node (called source vehicle) to a group of nodes that found in different locations (called destination vehicles). The purpose of this paper is to study the existing multicast routing protocols in VANET and produce good survey about them and determine the advantages and disadvantages of each one as well as classify them into different categories based on some effected parameters such as quality of service, vehicle trajectory and etc. After analyzing these routing protocols we concluded that there is persistent need to produce efficient multicast routing protocol in this network to decrease the resource consumption and improve the overall performance

HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising

The paper presents a novel approach for vector-floorplan generation via a diffusion model, which denoises 2D coordinates of room/door corners with two inference objectives: 1) a single-step noise as the continuous quantity to precisely invert the continuous forward process; and 2) the final 2D coordinate as the discrete quantity to establish geometric incident relationships such as parallelism, orthogonality, and corner-sharing. Our task is graph-conditioned floorplan generation, a common workflow in floorplan design. We represent a floorplan as 1D polygonal loops, each of which corresponds to a room or a door. Our diffusion model employs a Transformer architecture at the core, which controls the attention masks based on the input graph-constraint and directly generates vector-graphics floorplans via a discrete and continuous denoising process. We have evaluated our approach on RPLAN dataset. The proposed approach makes significant improvements in all the metrics against the state-of-the-art with significant margins, while being capable of generating non-Manhattan structures and controlling the exact number of corners per room. A project website with supplementary video and document is here https://aminshabani.github.io/housediffusion

Experimental study on the characteristics of formation and dissociation of CO2 hydrates in porous media

Geologic carbon sequestration (GCS) has pursued as a feasible strategy to store the large amount of CO2 to curb its emission to the atmosphere in an effort to mitigate the greenhouse effects. CO2 hydrate, which can form when the pressure and temperature satisfy its stability condition, can provide a self-trapping mechanism for an offshore CO2 geologic storage. For example, direct sequestration of CO2 in the form of hydrate cystals an be achieved in the storage zone an potentially provide a secondary caprock. These application, however, require a thorough understanding of the formation and dissociation of CO2 hydrates in porous media, which are largely unknown yet. In this manuscript, a laboratory study on the formation and dissociation of CO2 hydrates in two different environments, a two- (CO2-water) or three-phase (CO2-water in glass beads) condition, is presented. Based on the experimental results, it can be anticipated that the pressure and temperature change will be negligible when the formation of CO2 hydrate is induced for GCS in the actual soil/rock layers. Besides, the formation of CO2 hydrate in porous media may be faster, compared to the two-phase bulk condition that has been typically used in many laboratory studies, as solid grains help accelerate the hydrate formation by providing nucleus sites of crystals. Further elaborations on the role of solid grains would bring a clear path for the feasible application in the subsea area