Optimization of correlated multi-response quality engineering by the upside-down normal loss function

Most of the published literature on robust design is basically concerned with a single response. However, the reality is that common industrial problems usually involve several quality characteristics, which are often correlated. Traditional approaches to multidimensional quality do not offer much information on how much better or worse a process is when finding optimal settings. Köksoy and Fan [Engineering Optimization 44 (8): 935–945] pointed out that the upside-down normal loss function provides a more reasonable risk assessment to the losses of being off-target in product engineering research. However, they only consider the single-response case. This article generalizes their idea to more than one response under possible correlations and co-movement effects of responses on the process loss. The response surface methodology has been adapted, estimating the expected multivariate upside-down normal loss function of a multidimensional system to find the optimal control factor settings of a given problem. The procedure and its merits are illustrated through an example.     

Optimization of Multireservoir Systems by Genetic Algorithm

Application of optimization techniques for determining the optimal operating policy of reservoirs is a major issue in water resources planning and management. As an optimization Genetic Algorithm, ruled by evolution techniques, have become popular in diversified fields of science. The main aim of this study is to explore the efficiency and effectiveness of genetic algorithm in optimization of multi-reservoirs. A computer code has been constructed for this purpose and verified by means of a reference problem with a known global optimum. Three reservoirs in the Colorado River Storage Project were optimized for maximization of energy production. Besides, a real-time approach utilizing a blend of online and a posteriori data was proposed. The results obtained were compared to the real operational data and genetic algorithm was found to be effective and can be utilized as an alternative technique to other traditional optimization techniques.     

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

In this paper, we present a visionary concept referred to as Collaborative and Cognitive Network Platforms (CCNPs) as a future-proof solution for creating a dependable, self-organizing and self-managing communication substrate for effective ICT solutions to societal problems. CCNP creates a cooperative communication platform to support critical services across a range of business sectors. CCNP is based on the personal network (PN) technology which is an inherently cooperative environment prototyped in the Dutch Freeband PNP2008 and the European Union IST MAGNET projects. In CCNP, the cognitive control plane strives to exploit the resources to better satisfy the requirements of networked applications. CCNP facilitates collaboration inherently. Through cognition in the cognitive control plane, CCNP becomes a self-managed substrate. The self-managed substrate, in this paper, is defined as cognitive and collaborative middleware on which future applications run without user intervention. Endemic sensor networks may be incorporated into the CCNP concept to feed its cognitive control plane. In this paper, we present the CCNP concept and discuss the research challenges related to collaboration and cognition.     

Throughput and delay analysis of multi-channel wireless infrastructure networks

Wireless infrastructure networks (WINs) provide ubiquitous connectivity to mobile nodes in metro areas. The nodes in such backbone networks are often equipped with multiple transceivers to allow for concurrent transmissions in multiple orthogonal channels. In this study, we develop an analytical model for the estimation of the delay and throughput performance of wireless infrastructure networks employing slotted ALOHA channel access and slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) over multiple channels. The analytical model, which takes into account the correlation due to multi-hop transmissions, approximates the performance observed through simulations accurately.     

Performance measures for video object segmentation and tracking

We propose measures to evaluate quantitatively the performance of video object segmentation and tracking methods without ground-truth (GT) segmentation maps. The proposed measures are based on spatial differences of color and motion along the boundary of the estimated video object plane and temporal differences between the color histogram of the current object plane and its predecessors. They can be used to localize (spatially and/or temporally) regions where segmentation results are good or bad; and/or they can be combined to yield a single numerical measure to indicate the goodness of the boundary segmentation and tracking results over a sequence. The validity of the proposed performance measures without GT have been demonstrated by canonical correlation analysis with another set of measures with GT on a set of sequences (where GT information is available). Experimental results are presented to evaluate the segmentation maps obtained from various sequences using different segmentation approaches.     

Potential Anomaly Separation and Archeological Site Localization Using Genetically Trained Multi‐level Cellular Neural Networks

In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi‐level cellular neural network (ML‐CNN) is introduced, developed, and applied to real data. ML‐CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas‐Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.     

Cooperative Communications in Future Home Networks

The basic idea behind cooperative communications is that mobile terminals collaborate to send data to each other. This effectively adds diversity in the system and improves the overall performance. In this paper, we investigate the potential gains of cooperative communication in future home networks. We derive analytical expressions for the error probability of binary phase shift keying (BPSK) signals over Nakagami-m fading channels in a multi relay communication network. Following to the analytical study, we analyze the contribution of cooperative relaying to the 60GHz network connectivity through simulations using a realistic indoor environment model. We compare the performance of different relay configurations under variable obstacle densities. We show that a typical 60GHz indoor network should employ either a multi-relay configuration or a single-relay configuration with a smart relay selection mechanism to achieve acceptable outage rates. In the use of multiple-relay configuration, both analytical and simulation studies indicate that increasing the number of cooperative relays does not improve the system performance significantly after a certain threshold.     

Revisiting Slotted ALOHA: Density Adaptation in FANETs

Unmanned aerial vehicles have been widely used in many areas of life. They communicate with each other or infrastructure to provide ubiquitous coverage or assist cellular and sensor networks. They construct flying ad hoc networks. One of the most significant problems in such networks is communication among them over a shared medium. Using random channel access techniques is a useful solution. Another important problem is that the variations in the density of these networks impact the quality of service and introduce many challenges. This paper presents a novel density-aware technique for flying ad hoc networks. We propose Density-aware Slotted ALOHA Protocol that utilizes slotted ALOHA with a dynamic random access probability determined using network density in a distributed fashion. Compared to the literature, this paper concentrates on proposing a three-dimensional, easily traceable model and stabilize the channel utilization performance of slotted ALOHA with an optimized channel access probability to its maximum theoretical level, 1/e, where e is the Euler’s number. Monte-Carlo simulation results validate the proposed approach leveraging aggregate interference density estimator under the simple path-loss model. We compare our protocol with two existing protocols, which are Slotted ALOHA and Stabilized Slotted ALOHA. Comparison results show that the proposed protocol has 36.78% channel utilization performance; on the other hand, the other protocols have 24.74% and 30.32% channel utilization performances, respectively. Considering the stable results and accuracy, this model is practicable in highly dynamic networks even if the network is sparse or dense under higher mobility and reasonable non-uniform deployments.