Search results for: energy efficient clustering

Authors: Awatef Chniguir, Tarek Farah, Zouhair Ben Jemaa, Safya Belguith

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Optimal routing allows minimizing energy consumption in wireless sensor networks (WSN). Clustering has proven its effectiveness in organizing WSN by reducing channel contention and packet collision and enhancing network throughput under heavy load. Therefore, nowadays, with the emergence of the Internet of Things, heterogeneity is essential. Stable election protocol (SEP) that has increased the network stability period and lifetime is the first clustering protocol for heterogeneous WSN. SEP and its descendants, namely SEP, Threshold Sensitive SEP (TSEP), Enhanced TSEP (ETSSEP) and Current Energy Allotted TSEP (CEATSEP), were studied. These algorithms’ performance was evaluated based on different metrics, especially first node death (FND), to compare their stability. Simulations were conducted on the MATLAB tool considering two scenarios: The first one demonstrates the fraction variation of advanced nodes by setting the number of total nodes. The second considers the interpretation of the number of nodes while keeping the number of advanced nodes permanent. CEATSEP outperforms its antecedents by increasing stability and, at the same time, keeping a low throughput. It also operates very well in a large-scale network. Consequently, CEATSEP has a useful lifespan and energy efficiency compared to the other routing protocol for heterogeneous WSN.

Keywords: clustering, heterogeneous, stability, scalability, IoT, WSN

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: G. E. Kiral, O. Elma, A. T. Ince, B. Vural, U. S. Selamogullari, M. Uzunoglu

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Energy is an indispensable resource to meet the needs of people. Depending on the needs of people, the correct and efficient use of electrical energy has became important nowadays. Besides the need for the electrical energy is also increasing with the rapidly developing technology and continuously changing living standards. Due to the depletion of energy sources and increased demand for electricity, efficient energy use is an important research topic. Recently, ideas like smart cities, smart buildings and smart homes have been widely used under smart grid concept. With smart grid infrastructure, it will be possible to monitor electrical demand of a residential customer and control each electricity generation center for more efficient energy flow. The smallest component of the smart grid can be considered as smart homes. Better utilization of the electrical grid can be achieved through the communication of the smart home with both other customers in the grid and appliances in the house itself since generation can effectively be scheduled by having more precise demand data. Smart Plugs are used for the communication with the household appliances in the house. Smart Plug is an intermediate control element, which can be mounted on the existing outlet, and thus can be used to monitor the energy consumption of the plugged device and also can provide on/off control energy remotely. This study proposes a Smart Plug for energy monitoring and energy management. Proposed design is composed of five subsystems: micro controller embedded system with communication system, metering circuitry, power supply and switching circuitry. The developed smart plug offers efficient use of electrical energy.

Keywords: energy efficiency, home energy management, smart home, smart plug

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Authors: Rafia Akbar

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This paper assesses different Energy Efficiency Measures (EEMs) and their impact on energy consumption and carbon footprint of an educational building located in Islamabad. A base case was first developed in accordance with typical construction practices in Pakistan. Several EEMs were separately applied to the baseline design to quantify their impact on operational energy reduction of the building and the resultant carbon emissions. Results indicate that by applying these measures, there is a potential to reduce energy consumption up to 49% as compared to the base case. It was observed that energy efficient ceiling fans and lights, insulation of the walls and roof and an efficient air conditioning system for the building can provide significant energy savings. The results further indicate that the initial investment cost of these energy efficiency measures can be recovered within 6 to 7 years of building’s service life.

Keywords: CO2 savings, educational building, energy efficiency measures, payback period

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Authors: Ambalika Ekka

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In today’s world, the growing demand for urban built forms has resulted in the production and consumption of building materials i.e. embodied energy in building construction, leading to pollution and greenhouse gas (GHG) emissions. Therefore, new buildings will offer a unique opportunity to implement more energy efficient building without compromising on building performance of the building. Embodied energy of building materials forms major contribution to embodied energy in buildings. The paper results in an approach towards designing an energy efficient apartment building through embodied energy assessment. This paper discusses the trend of residential development in Rourkela, which includes three case studies of the contemporary houses, followed by architectural elements, number of storeys, predominant material use and plot sizes using primary data. It results in identification of predominant material used and other characteristics in urban area. Further, the embodied energy coefficients of various dominant building materials and alternative materials manufactured in Indian Industry is taken in consideration from secondary source i.e. literature study. The paper analyses the embodied energy by estimating materials and operational energy of proposed building followed by altering the specifications of the materials based on the building components i.e. walls, flooring, windows, insulation and roof through res build India software and comparison of different options is assessed with consideration of sustainable parameters. This paper results that autoclaved aerated concrete block only reaches the energy performance Index benchmark i.e. 69.35 kWh/m² yr i.e. by saving 4% of operational energy and as embodied energy has no particular index, out of all materials it has the highest EE 23206202.43  MJ.

Keywords: energy efficient, embodied energy, EPI, building materials

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Authors: Zakariya B. H., Idris M. I., Jungudo M. A.

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High energy consumptions of urban settlements in Nigeria are escalating due to strong population growth and migration as a result of crises. The demand for lighting, heating, ventilation and air conditioning (LHVAC) is becoming higher. Conversely, there is a poor electricity supply to both rural and urban settlement in Nigeria. Generators were mostly used in Nigeria as a source of energy for LHVAC. Energy efficiency can be defined as any measure taken to reduce the amount of energy consumed for heating ventilation and air-conditioning (HVAC), and house hold appliances like computers, stoves, refrigerators, televisions etc. The aim of the study was to minimize energy consumption in building through the integration of energy efficiency and renewable energy in building sector. Some of the energy efficient buildings within the study area were identified, the study covers there major cities of Nigeria namely, Abuja, Kaduna and Lagos city. The cost of investment on the energy efficiency and renewable energy was determined and compared with other fossil energy source for conventional building. Findings revealed that the low energy and energy efficient buildings in Nigeria are cheaper than the conventional ones. Based on the finding of the research, construction stake holders are strongly encouraged to abandon the conventional buildings and consider energy efficiency and renewable energy in buildings.

Keywords: energy, efficiency, LHVAC, sustainable development

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

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Authors: Amit Verma, Pardeep Kaur

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In software development organizations, Component based Software engineering (CBSE) is emerging paradigm for software development and gained wide acceptance as it often results in increase quality of software product within development time and budget. In component reusability, main challenges are the right component identification from large repositories at right time. The major objective of this work is to provide efficient algorithm for storage and effective retrieval of components using neural network and parameters based on user choice through clustering. This research paper aims to propose an algorithm that provides error free and automatic process (for retrieval of the components) while reuse of the component. In this algorithm, keywords (or components) are extracted from software document, after by applying k mean clustering algorithm. Then weights assigned to those keywords based on their frequency and after assigning weights, ANN predicts whether correct weight is assigned to keywords (or components) or not, otherwise it back propagates in to initial step (re-assign the weights). In last, store those all keywords into repositories for effective retrieval. Proposed algorithm is very effective in the error correction and detection with user base choice while choice of component for reusability for efficient retrieval is there.

Keywords: component based development, clustering, back propagation algorithm, keyword based retrieval

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Authors: Li Ni, Pen ManMan, Li KenLi

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Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: chemical reaction optimization, expection maimization, initia, objective function clustering

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Authors: Lijuan Zhou, Mengqi Wu, Changyong Niu

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Attributed graph clustering can utilize the graph topology and node attributes to uncover hidden community structures and patterns in complex networks, aiding in the understanding and analysis of complex systems. Utilizing contrastive learning for attributed graph clustering can effectively exploit meaningful implicit relationships between data. However, existing attributed graph clustering methods based on contrastive learning suffer from the following drawbacks: 1) Complex data augmentation increases computational cost, and inappropriate data augmentation may lead to semantic drift. 2) The selection of positive and negative samples neglects the intrinsic cluster structure learned from graph topology and node attributes. Therefore, this paper proposes a method called self-supervised Attributed Graph Clustering with Dual Contrastive Loss constraints (AGC-DCL). Firstly, Siamese Multilayer Perceptron (MLP) encoders are employed to generate two views separately to avoid complex data augmentation. Secondly, the neighborhood contrastive loss is introduced to constrain node representation using local topological structure while effectively embedding attribute information through attribute reconstruction. Additionally, clustering-oriented contrastive loss is applied to fully utilize clustering information in global semantics for discriminative node representations, regarding the cluster centers from two views as negative samples to fully leverage effective clustering information from different views. Comparative clustering results with existing attributed graph clustering algorithms on six datasets demonstrate the superiority of the proposed method.

Keywords: attributed graph clustering, contrastive learning, clustering-oriented, self-supervised learning

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Authors: Gagandeep Singh, Navdeep Singh

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Due to limited battery of sensor nodes, so energy efficiency found to be main constraint in WSN. Therefore the main focus of the present work is to find the ways to minimize the energy consumption problem and will results; enhancement in the network stability period and life time. Many researchers have proposed different kind of the protocols to enhance the network lifetime further. This paper has evaluated the issues which have been neglected in the field of the WSNs. WSNs are composed of multiple unattended ultra-small, limited-power sensor nodes. Sensor nodes are deployed randomly in the area of interest. Sensor nodes have limited processing, wireless communication and power resource capabilities Sensor nodes send sensed data to sink or Base Station (BS). I-LEACH gives adaptive clustering mechanism which very efficiently deals with energy conservations. This paper ends up with the shortcomings of various adaptive clustering based WSNs protocols.

Keywords: WSN, I-Leach, MATLAB, sensor

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Authors: Sadia Basar, Naila Habib, Awais Adnan

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The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

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Authors: Loai Abdallah, Malik Yousef

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A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance.

Keywords: ensemble clustering, decision trees, classification, K nearest neighbors

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Authors: Francky Fouedjio

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Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical clustering methods.

Keywords: clustering, geostatistics, multivariate data, non-parametric

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Authors: Taysir Soliman

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One of the current popular clustering techniques is Spectral Clustering (SC) because of its advantages over conventional approaches such as hierarchical clustering, k-means, etc. and other techniques as well. However, one of the disadvantages of SC is the time consuming process because it requires computing the eigenvectors. In the past to overcome this disadvantage, a number of attempts have been proposed such as the Power Iteration Clustering (PIC) technique, which is one of versions from SC; some of PIC advantages are: 1) its scalability and efficiency, 2) finding one pseudo-eigenvectors instead of computing eigenvectors, and 3) linear combination of the eigenvectors in linear time. However, its worst disadvantage is an inter-class collision problem because it used only one pseudo-eigenvectors which is not enough. Previous researchers developed Deflation-based Power Iteration Clustering (DPIC) to overcome problems of PIC technique on inter-class collision with the same efficiency of PIC. In this paper, we developed Parallel DPIC (PDPIC) to improve the time and memory complexity which is run on apache spark framework using sparse matrix. To test the performance of PDPIC, we compared it to SC, ESCG, ESCALG algorithms on four small graph benchmark datasets and nine large graph benchmark datasets, where PDPIC proved higher accuracy and better time consuming than other compared algorithms.

Keywords: spectral clustering, power iteration clustering, deflation-based power iteration clustering, Apache spark, large graph

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Authors: Salima Kouici, Abdelkader Khelladi

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In this work, we begin with the presentation of the Tθ family of usual similarity measures concerning multidimensional binary data. Subsequently, some properties of these measures are proposed. Finally, the impact of the use of different inter-elements measures on the results of the Agglomerative Hierarchical Clustering Methods is studied.

Keywords: binary data, similarity measure, Tθ measures, agglomerative hierarchical clustering

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Authors: Alhadi Bustaman, Soeganda Formalidin, Titin Siswantining

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DNA microarray technology is used to analyze thousand gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Biclustering algorithm has been used as an alternative approach to identifying structures from gene expression data. In this paper, we introduce a transform technique based on singular value decomposition to identify normalized matrix of gene expression data followed by Mixed-Clustering algorithm and the Lift algorithm, inspired in the node-deletion and node-addition phases proposed by Cheng and Church based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard datasets demonstrated the effectiveness of the algorithm in gene expression data.

Keywords: agglomerative hierarchical clustering (AHC), biclustering, gene expression data, lymphoma, singular value decomposition (SVD)

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Authors: Yu-Ting Tsai, Chiun-Chieh Hsu, Yu-Chun Chu

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In recent years, due to the development of wireless network technology, many researchers have devoted to the study of wireless sensor networks. The applications of wireless sensor network mainly use the sensor nodes to collect the required information, and send the information back to the users. Since the sensed area is difficult to reach, there are many restrictions on the design of the sensor nodes, where the most important restriction is the limited energy of sensor nodes. Because of the limited energy, researchers proposed a number of ways to reduce energy consumption and balance the load of sensor nodes in order to increase the network lifetime. In this paper, we proposed the Energy-Balanced Clustering method with Auxiliary Members on Wireless Sensor Networks（EBCAM）based on the cluster routing. The main purpose is to balance the energy consumption on the sensed area and average the distribution of dead nodes in order to avoid excessive energy consumption because of the increasing in transmission distance. In addition, we use the residual energy and average energy consumption of the nodes within the cluster to choose the cluster heads, use the multi hop transmission method to deliver the data, and dynamically adjust the transmission radius according to the load conditions. Finally, we use the auxiliary cluster members to change the delivering path according to the residual energy of the cluster head in order to its load. Finally, we compare the proposed method with the related algorithms via simulated experiments and then analyze the results. It reveals that the proposed method outperforms other algorithms in the numbers of used rounds and the average energy consumption.

Keywords: auxiliary nodes, cluster, load balance, routing algorithm, wireless sensor network

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Authors: H. Aoudia, Y. Touati, E. H. Teguig, A. Ali Cherif

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Conceiving and developing routing protocols for wireless sensor networks requires considerations on constraints such as network lifetime and energy consumption. In this paper, we propose a hybrid hierarchical routing protocol named HHRP combining both clustering mechanism and multipath optimization taking into account residual energy and RSSI measures. HHRP consists of classifying dynamically nodes into clusters where coordinators nodes with extra privileges are able to manipulate messages, aggregate data and ensure transmission between nodes according to TDMA and CDMA schedules. The reconfiguration of the network is carried out dynamically based on a threshold value which is associated with the number of nodes belonging to the smallest cluster. To show the effectiveness of the proposed approach HHRP, a comparative study with LEACH protocol is illustrated in simulations.

Keywords: routing protocol, optimization, clustering, WSN

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Authors: Billel Kenidra, Mohamed Benmohammed

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Data mining technique used in the field of clustering is a subject of active research and assists in biological pattern recognition and extraction of new knowledge from raw data. Clustering means the act of partitioning an unlabeled dataset into groups of similar objects. Each group, called a cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Several clustering methods are based on partitional clustering. This category attempts to directly decompose the dataset into a set of disjoint clusters leading to an integer number of clusters that optimizes a given criterion function. The criterion function may emphasize a local or a global structure of the data, and its optimization is an iterative relocation procedure. The K-Means algorithm is one of the most widely used partitional clustering techniques. Since K-Means is extremely sensitive to the initial choice of centers and a poor choice of centers may lead to a local optimum that is quite inferior to the global optimum, we propose a strategy to initiate K-Means centers. The improved K-Means algorithm is compared with the original K-Means, and the results prove how the efficiency has been significantly improved.

Keywords: microarray data mining, biological pattern recognition, partitional clustering, k-means algorithm, centroid initialization

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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Clustering is a well known data mining technique used in pattern recognition and information retrieval. The initial dataset to be clustered can either contain categorical or numeric data. Each type of data has its own specific clustering algorithm. In this context, two algorithms are proposed: the k-means for clustering numeric datasets and the k-modes for categorical datasets. The main encountered problem in data mining applications is clustering categorical dataset so relevant in the datasets. One main issue to achieve the clustering process on categorical values is to transform the categorical attributes into numeric measures and directly apply the k-means algorithm instead the k-modes. In this paper, it is proposed to experiment an approach based on the previous issue by transforming the categorical values into numeric ones using the relative frequency of each modality in the attributes. The proposed approach is compared with a previously method based on transforming the categorical datasets into binary values. The scalability and accuracy of the two methods are experimented. The obtained results show that our proposed method outperforms the binary method in all cases.

Keywords: clustering, unsupervised learning, pattern recognition, categorical datasets, knowledge discovery, k-means

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Authors: P. Nafisi Poor, P. Javid

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Over the past few years, the idea of adaptive buildings and specifically, adaptive building energy management systems (ABEMS) has become popular. Well-performed management in terms of energy is to create a balance between energy consumption and user comfort; therefore, in new energy management models, efficient energy consumption is not the sole factor and the user's comfortability is also considered in the calculations. One of the main ways of measuring this factor is by analyzing user feedback on the conditions to understand whether they are satisfied with conditions or not. This paper provides a comprehensive review of recent approaches towards energy management systems based on users' feedbacks and subsequently performs a comparison between them premised upon their efficiency and accuracy to understand which approaches were more accurate and which ones resulted in a more efficient way of minimizing energy consumption while maintaining users' comfortability. It was concluded that the highest accuracy rate among the presented works was 95% accuracy in determining satisfaction and up to 51.08% energy savings can be achieved without disturbing user’s comfort. Considering the growing interest in designing and developing adaptive buildings, these studies can support diverse inquiries about this subject and can be used as a resource to support studies and researches towards efficient energy consumption while maintaining the comfortability of users.

Keywords: adaptive buildings, energy efficiency, intelligent buildings, user comfortability

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Authors: Aldona Kluczek

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In recent years, manufacturing processes have interacted with sustainability issues realized in the cost-effective ways that minimalize energy, decrease negative impacts on the environment and are safe for society. However, the attention has been on separate sustainability assessment methods considering energy and material flow, energy consumption, and emission release or process control. In this paper, the energy-led sustainability assessment approach combining the methods: energy Life Cycle Assessment to assess environmental impact, Life Cycle Cost to analyze costs, and Social Life Cycle Assessment through ‘energy LCA-based value stream map’, is used to assess the energy sustainability of the hardwood lumber manufacturing process in terms of technologies. The approach integrating environmental, economic and social issues can be visualized in the considered energy-efficient technologies on the map of an energy LCA-related (input and output) inventory data. It will enable the identification of efficient technology of a given process to be reached, through the effective analysis of energy flow. It is also indicated that interventions in the considered technology should focus on environmental, economic improvements to achieve energy sustainability. The results have indicated that the most intense energy losses are caused by a cogeneration technology. The environmental impact analysis shows that a substantial reduction by 34% can be achieved with the improvement of it. From the LCC point of view, the result seems to be cost-effective, when done at that plant where the improvement is used. By demonstrating the social dimension, every component of the energy of plant labor use in the life-cycle process of the lumber production has positive energy benefits. The energy required to install the energy-efficient technology amounts to 30.32 kJ compared to others components of the energy of plant labor and it has the highest value in terms of energy-related social indicators. The paper depicts an example of hardwood lumber production in order to prove the applicability of a sustainability assessment method.

Keywords: energy efficiency, energy life cycle assessment, life cycle cost, social life cycle analysis, manufacturing process, sustainability assessment

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Authors: Christian H. Sanabria-Montaña, Rodrigo Huerta-Quintanilla

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A lattice network is a special type of network in which all nodes have the same number of links, and its boundary conditions are periodic. The most basic lattice network is the ring, a one-dimensional network with periodic border conditions. In contrast, the Cartesian product of d rings forms a d-dimensional lattice network. An analytical expression currently exists for the clustering coefficient in this type of network, but the theoretical value is valid only up to certain connectivity value; in other words, the analytical expression is incomplete. Here we obtain analytically the clustering coefficient expression in d-dimensional lattice networks for any link density. Our analytical results show that the clustering coefficient for a lattice network with density of links that tend to 1, leads to the value of the clustering coefficient of a fully connected network. We developed a model on criminology in which the generalized clustering coefficient expression is applied. The model states that delinquents learn the know-how of crime business by sharing knowledge, directly or indirectly, with their friends of the gang. This generalization shed light on the network properties, which is important to develop new models in different fields where network structure plays an important role in the system dynamic, such as criminology, evolutionary game theory, econophysics, among others.

Keywords: clustering coefficient, criminology, generalized, regular network d-dimensional

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Authors: Hugh Byrd, Steve Matthewnan

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The devastation of the city centre of Christchurch, New Zealand, after the 2010 and 2011 earthquakes presented an opportunity to rebuild infrastructure in a coordinated and efficient manner to allow for a city that was energy efficient, low carbon, resilient and provided both energy security and justice. The research described in this paper records the processes taken to attempt to rebuild the energy infrastructure. The story is one of political decisions overriding appropriate technology and ultimately is a lesson in how not to handle the implementation of post-disaster energy infrastructure. Lack of clarity in decision making by central government and then not pursuing consultant’s recommendations led to a scheme that was effectively abandoned in 2016 and described as ‘a total failure’. The paper records the critical events that occurred and explains why the proposed energy infrastructure was both politically and technologically inappropriate.

Keywords: energy infrastructure, policy and governance, post-disaster rebuilding

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Yulia A. Kononova, Znang X. Ning

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Nowadays, architectural development is getting faster and faster. Nevertheless, modern architecture often does not meet all the points, which could help our planet to get better. As we know, people are spending an enormous amount of energy every day of their lives. Because of the uncontrolled energy usage, people have to increase energy production. As energy production process demands a lot of fuel sources, it courses a lot of problems such as climate changes, environment pollution, animals’ distinction, and lack of energy sources also. Nevertheless, nowadays humanity has all the opportunities to change this situation. Architecture is one of the most popular fields where it is possible to apply new methods of saving energy or even creating it. Nowadays we have kinds of buildings, which can meet new willing. One of them is energy effective buildings, which can save or even produce energy, combining several energy-saving principles. The main aim of this research is to provide information that helps to apply energy-saving methods while designing an environment-friendly building. The research methodology requires gathering relevant information from literature, building guidelines documents and previous research works in order to analyze it and sum up into a material that can be applied to energy-efficient building design. To mark results it should be noted that the usage of all the energy-saving methods applied to a design project of building results in ultra-low energy buildings that require little energy for space heating or cooling. As a conclusion it can be stated that developing methods of passive house design can decrease the need of energy production, which is an important issue that has to be solved in order to save planet sources and decrease environment pollution.

Keywords: accumulation, energy-efficient building, storage, superinsulation, passive house

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Authors: Ahmed Khalil

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Increased energy demands around the world have led to the raise in power production which has resulted with more greenhouse gas emissions through fossil sources. These fossil sources and emissions cause deterioration in echo-system. Therefore, renewable energy sources come to the scene as echo-friendly and clean energy sourcing, whereas the electrical devices and energy needs decrease in the timeline. Each of these renewable energy sources contribute to the reduction of greenhouse gases and mitigate environmental deterioration. However, there are also some general and source-specific challenges, which influence the choice of the investors. The most prominent general challenge that effects end-users’ comfort and reliability is usually determined as the intermittence which derives from the diversions of source conditions, due to nature dynamics and uncontrolled periodic changes. Research and development professionals strive to mitigate intermittence challenge through material improvement for each renewable source whereas hybrid source mix stand as a solution. This solution prevails well, when single renewable technologies are upgraded further. On the other hand, integration of energy efficient devices and systems, raise the affirmative effect of such solution in means of less energy requirement in sustainability composition or scenario. This paper provides a glimpse on the advantages of composing renewable source mix versus single usage, with contribution of sampled e-efficient systems and devices. Accordingly it demonstrates the extended benefits, through planning and predictive estimation stages of Ahmadi Town Projects in Kuwait.

Keywords: e-efficient systems, hybrid source, intermittence challenge, renewable energy

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Authors: Ghodhbani Salah, Jemili Farah

Abstract:

With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.

Keywords: intrusion detection system, alarm filtering, ANT class, ant clustering, intruders’ behaviors, false alarms

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Authors: Mas Aji Rizki Widjayanto, Rizka Yunita

Abstract:

The building which has an efficient and low-energy today followed by the developers. It’s not because trends on the building nowaday, but rather because of its positive effects in the long term, where the cost of energy per month to be much cheaper, along with the high price of electricity. The use of solar power (Photovoltaic System) becomes one source of electrical energy for the apartment so that will efficiently use energy, water, and other resources in the operations of the apartment. However, more than 80% of the solar radiation is not converted into electrical energy, but reflected and converted into heat energy. This causes an increase on the working temperature of solar panels and consequently decrease the efficiency of conversion to electrical energy. The high temperature solar panels work caused by solar radiation can be used as medium heat exchanger or heating water for the apartments, so that the working temperature of the solar panel can be lowered to reduce the reduction on the efficiency of conversion to electrical energy.

Keywords: photovoltaic system, efficient, heat energy, heat exchanger, efficiency of conversion

Procedia PDF Downloads 350