INTEGRATED NETWORK DEVELOPMENT & PLANNING - from forecasting to grid screening and concepts -

Synaptic plasticity is often explored as a form of unsupervised adaptation in cortical microcircuits to learn the structure of complex sensory inputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data. In this work, input-specific structural changes are analyzed for three empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM) plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks. It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by the presentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network. To solve the problem of interference, we suggest that models of plasticity be extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the case in experimental neuroscience.

Spectrum sensing in cognitive radio network plays a vital role in the efficient utilization of underutilized licensed band. Efficient utilization implies that whenever spectrum is not being used by primary user it should be assign to secondary user. Conventional structure of spectrum sensing using energy detection technique cannot guarantee to fulfill the interest of primary user and secondary user simultaneously. Primary user wants to maximize the probability of detection whereas secondary user wants to minimize probability of false alarm. This paper analyzes an adaptive structure of spectrum sensing which is based on the trade-off between the interest of primary user and secondary user. Adaptive structure tries to minimize the probability of error in allocation of frequency band among primary user and secondary user. We derive an expression of optimum number of sample required which minimizes the probability of error, for accurate estimation at different parameter like SNR and α. The adaptive structure demonstrates the better utilization of frequency band by having better probability of detection and lesser probability of false alarm.

It is crucial for the integral future development of the power grid to measure grid planning rationality during the rapid growth period of it. Through the analysis of the evaluation characteristics and influencing factors of China's power grid planning, this paper determined the value orientation of the grid planning assessment and evaluation content based on a quantitative evaluation of the existing power grid planning model, and established comprehensive assessment index system of grid planning covering security, economic, excellence and coordination based on the safe and stable operation of the power grid, and proposed optimize weight assessment methods applicable to the assessment of the overall level of grid planning based on improved particle swarm optimization. Finally this paper provides the rationality and feasibility of the comprehensive evaluation index system and optimized weight assessment method through engineering empirical analysis.

Our research focuses on the evaluation of digital space and its prospective use as a functional space for future architectural planning tasks. Through the existence of digital technologies the authors consider that architects will have to expose themselves to new - planning tasks, which are not comparable to traditional architectural duties. This requires us to rethink architectural terms and their meaning in digital space. It demands us to extend the borders within which we think, work and design. The paper presents exemplary projects that demonstrate the applicability and added value of these mergers as an extension of solid architectural buildings. Focus is laid onto digital architecture that either extends its analog counterpart by providing functionality that does not exist in the "real" world or which doesn¥t have an analog counterpart at all. The paper aims at the definition of new tasks for architects to be developed. It describes methods and strategies architects have to be aware of to be capable to offer an extended field of activity. Finally it presents exemplary projects which show the possible added value clients could gain from buildings to be established as mergers of analog and digital spaces.

This paper brings forward two kinds of PD control schemes of adaptive neural-variable structure for uncertain robot trajectory tracking. The first scheme consists of a PD feedback and a dynamic compensator which is composed of RBF neural network and variable structure. The adaptive laws of Network weights are based on Lyapunov function method. This controller can guarantee stability of closed-loop system and asymptotic convergence of tracking errors. The second scheme substitutes the integrated controller consisting of neural network and variable structure for the hybrid controller by way of smooth function. This integrated controller can reduce chattering of variable structure control input, overcome the deficiencies of local generalization neural networks and improve control precision and convergence speed. In addition, This controller is still able to ensure the system maintains good robustness and stability in the case of neural network disabled. The simulation results have showed the effectiveness of two kinds of control schemes, and that the second scheme is more advantageous.

The theory of complex networks has been studied extensively since its inception. However, until now, the impact of the node-type distributions is related to network topology and cannot be evaluated independently. In this paper, a network structure is modeled via an adjacency matrix (network topology) and a set of node type distribution vectors. Three specific issues that need to be considered for node type distributions in smart grid testing and planning are summarized in this paper. First, a set of metrics are proposed and defined to evaluate the impact of node-type distributions on network performance independently. Second, another metric named the generation distribution factor is proposed to evaluate the distribution of generation buses resulting from the specific function and purpose of power grids and by considering the distribution of load buses as given conditions. Third, another metric, i.e., the power supply redundancy metric based on entropy, is proposed to evaluate the inequality of load in power supply. Finally, a discrimination factor is defined to ensure the overall evaluation and comparison of different networks is made for this inequality. All proposed metrics can be applied to the IEEE-30, IEEE-118, IEEE-300 bus systems, as well as Italian power grid components. The simulation results indicate that the IEEE-118 system has the best node type distribution and minimum discrimination; the Italian system has the worst node-type distribution and most serious discrimination of load power supply.

The collaborative European funded research and development landscape drives competitiveness among innovative organisations. Recently it has seen the rise of public private partnerships significantly impacting the dynamics of these networks. Thus, the complexity of managing research networks has intensified with the increased diversity of research network members. Additionally, the emergence of the academic entrepreneur has augmented the focus of educational institutions to include innovation and building start-up organisations. The impact of research is scalable if an optimum research network is created and managed effectively. This paper investigates network embeddedness; the nature of relationships, links and nodes within a research network, specifically their structure, configuration and quality. The contribution of this paper extends our understanding for establishing and maintaining effective collaborative research networks. The effects of network embeddedness are recognized in the literature as pertinent to innovation and the economy. Network theory literature claims that networks are essential to innovative clusters such as Silicon valley and innovation in high tech industries. The concept of embeddedness is what differentiates network theory from economic theory. This study adopts a qualitative approach and uncovers some of the challenges of multi-disciplinary research through case study insights. One challenge is competition between network members over ownership and sharing of data. The contribution of this paper recommends the establishment of scaffolding to accommodate cooperation in research networks, role appointment, and addressing contextual complexities early to avoid problem cultivation. Furthermore, it suggests recommendations in relation to network formation, incubation and operations. The network capability is enhanced by the recognition of network theory, open innovation and social exchange with the understanding that the network structure has an impact on innovation and social exchange in research networks and subsequently on research output. The research concludes that the success of collaborative research is reliant upon establishing a common language and understanding between network members to realise their research objectives.

Ports play an important role in maintaining the effectiveness of maritime logistics. When ports encounter congestion, strikes, or natural disasters, the maritime container transportation network might be significantly affected. The Beibu Gulf sea area is a key channel to supporting China’s participation in international economic cooperation in the western region. It is highly susceptible to the influence of the political and economic instability. This study introduces a dual-component framework to analyze the inherent structure and potential vulnerabilities of the container transportation network in the Beibu Gulf Sea areas. The findings show that the core layer of the network exhibited circular solidification characteristics. The entire network heavily relies on some core ports, such as Haiphong Port, Ho Chi Minh Port, and Qinzhou Port, and it highlights the potential increases in vulnerability. The finding shows that deliberate attacks have a greater impact than random attacks on the normal operations of maritime networks. If ports with high intermediary centrality are attacked, the connectivity and transportation efficiency of the Beibu Gulf maritime network will be significantly affected. However, under such circumstances, redistributing cargo transportation through route adjustments can deal with the transmission of cascading failures and maintain the network’s resilience. Based on the existing knowledge and the data collected in a case study, this research stands out as the first to provide a critical examination of the spatial structure and vulnerability of container shipping networks in the Beibu Gulf sea.

The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system.

Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and applied to remote sensing and other fields. For aerial platforms, lightweight structure and multimodal adaptations of convolutional neural network (CNN) models are urgently needed. Due to the limited model size, the performance of existing lightweight AOOD methods is low, especially in multimodal tasks. In this paper, a multimodal knowledge distillation (MKD) method is proposed for AOOD in aerial images. In MKD, a multimodal dynamic label assignment strategy is designed to select the optimal positive samples dynamically to adapt to different modalities and environments. Different multimodal localization and feature distillation modules are designed to make multimodal knowledge to be complementary and effectively learned by the lightweight model. Experiments on the public dataset demonstrated the effectiveness and advancement of MKD.

This paper proposes a technique for clustering features into profile groups to obtain optimum enhancement weights for reconstructing high resolution images. Neuro-fuzzy model, which combines the fuzzy reasoning behavior with the adaptive learning capability and connectionist structure of neural networks, is adopted to analyze and learn with gradient data and statistics and to generate gradient profile priors. In enhancement process, the optimum weights are appropriately chosen according to the gradient profile priors. From the experimental results, the proposed algorithm demonstrates quantitative performance improvement in classifying data and perceptual improvement in recovery of the high-resolution image.

This study examines the role of socio-technical dynamic resources and capabilities in enhancing the resilience of freight transport firms. Focusing on unique characteristics of freight transport sector, this study considers human capital and Industry 4.0 as socio-technical resources whereas network integration and operations planning as socio-technical capabilities. A survey is conducted on freight transport managers in the UK and partial least squares structural equation modelling (PLS-SEM) is employed to analyse the data. Our findings indicate that human capital and Industry 4.0 positively affect network integration and operations planning, which in turn enhance perceived resilience. Among two dynamic capabilities, network integration has a stronger impact on resilience than operations planning. Mediation analysis shows technical resources and capabilities partially enhance the impact of social resources and capabilities in SEM model. Our results suggest that socio resources and capabilities are at least as important as technical ones for building resilience in the freight sector. Practical and policy implications in our study suggest that freight transport firms and policy makers should invest and support skill-building initiatives, digital tools, supply chain analytics and collaboration platforms to increase freight transport resilience.

The massive integration of dispersed generation of combined heat and power units (CHP units) brings up challenges for today’s distribution networks that they not have been planned for. However, it is not yet fully understood in which areas and under which precondition, CHP units shall be installed and could be efficiently operated with minimum environmental impacts. In this paper an analysis of different energy supply scenarios for different distribution networks in Germany in 2030 evaluating the environmental impacts and the effects on grid planning is presented. Based on a comparison of these scenarios recommendations are given in which areas and under which preconditions a local heat and power supply with CHP units is more environmental friendly compared to centralized power generation in large power plants combined with conventional heating systems. INTRODUCTION Environmental protection and the efficient utilization of natural resources is the main aim of a sustainable energy supply assuring availability of energy with taking economical aspects into account. For ensuring a sustainable energy supply for the end-user the components of future power grids, high efficient dispersed generation units and optimized distribution networks with minimized losses and emissions are necessary. Until now these technologies are often optimized only in one of the following sustainable criteria: • Best Economical Value • Highest technical Efficiency Additionally, there is a need to evaluate whether the massive integration of dispersed generation units (wind, biomass, photovoltaic) provides a more environmental friendly energy supply or if it involves contrary effects, e.g. the need to provide either additional reserve power or energy storage devices for fluctuating generation. Thus, as an important step towards a reliable and economical and ecological optimized future power supply, it is vital to develop new energy supply scenarios that meet the future energy demand in distribution networks with reduced heat consumption and a higher electricity demand caused by new e-mobility applications and a higher penetration of consumer electronics. First the trends in power and heat consumption in Germany until 2030 have to be analyzed. TRENDS IN ENERGY CONSUMPTION AND GENERATION IN GERMANY The overall trend of electricity consumption from 2000 to 2030 in Germany can be estimated as nearly constant by analyzing the sectors industry, private households, trade and commerce and transportation as shown in figure 1. 0 50 100 150 200 25

PurposeThis study investigates how a firm's structural embeddedness, the structural position in a supply network that consists of major customers, influences the acquisition of supplier trade credit. Specifically, this study examines how network interconnectedness, network integration and network independence of a firm affect its ability to acquire supplier trade credit.Design/methodology/approachThis study utilizes financial data from Compustat to build a longitudinal dataset of manufacturing firms from 1998 to 2013. Customer segment disclosure data are used to construct firm-level network variables. A fixed effect regression approach is used for estimation.FindingsThe study results show that network interconnectedness is negatively associated with supplier trade credit, while network integration is positively associated with supplier trade credit. Network independence does not influence the extent of supplier trade credit. The post hoc analysis shows that the effects of the hypothesized factors vary under different product categories and credit ratings.Originality/valueThis study broadens the supply chain finance literature by showing how a firm's embedded network structural position can influence its ability to obtain supplier trade credit.

Despite the dominance of the structural approach in social network research, recent decades have witnessed an increasing emphasis on bringing individual psychology back into network analysis. The integration of network and psychological perspectives notwithstanding, numerous network and psychological phenomena are underexplored. This symposium provides an opportunity to showcase recent research endeavors that integrate social networks and social psychology. Using a variety of research methods, our papers address domains including multiplex ties and trust, lay beliefs and dormant tie utilization, brokerage and personality change, and motivation and network structures. Friendships, Business Roles, and Asymmetric Distrust Perceptions Toward Contracts Presenter: Lei Liu; UCL School of Management Lay Theories of Social Relations on Help-Seeking from Dormant Ties Presenter: Jung Won Lee; ESSEC Business School Presenter: Ko Kuwabara; INSEAD From Stability Comes Change: The Effects of Brokerage on Personality Change and Performance Presenter: Stefano Tasselli; Rotterdam School of Management, Erasmus U. Presenter: Martin J. Kilduff; UCL School of Management Goals and Social Networks: The Network Goal Analysis of Organizational Behavior Presenter: James D. Westaby; Columbia U. Presenter: Adam K. Parr; Columbia U.