Visible to the public Biblio

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2020-02-18
Griffioen, Paul, Weerakkody, Sean, Sinopoli, Bruno.  2019.  An Optimal Design of a Moving Target Defense for Attack Detection in Control Systems. 2019 American Control Conference (ACC). :4527–4534.
In this paper, we consider the problem of designing system parameters to improve detection of attacks in control systems. Specifically, we study control systems which are vulnerable to integrity attacks on sensors and actuators. We aim to defend against strong model aware adversaries that can read and modify all sensors and actuators. Previous work has proposed a moving target defense for detecting integrity attacks on control systems. Here, an authenticating subsystem with time-varying dynamics coupled to the original plant is introduced. Due to this coupling, an attack on the original system will affect the authenticating subsystem and in turn be revealed by a set of sensors measuring the extended plant. Moreover, the time-varying dynamics of the extended plant act as a moving target, preventing an adversary from developing an effective adaptive attack strategy. Previous work has failed to consider the design of the time-varying system matrices and as such provides little in terms of guidelines for implementation in real systems. This paper proposes two optimization problems for designing these matrices. The first designs the auxiliary actuators to maximize detection performance while the second designs the coupling matrices to maximize system estimation performance. Numerical examples are presented that validate our approach.
Gotsman, Alexey, Lefort, Anatole, Chockler, Gregory.  2019.  White-Box Atomic Multicast. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :176–187.
Atomic multicast is a communication primitive that delivers messages to multiple groups of processes according to some total order, with each group receiving the projection of the total order onto messages addressed to it. To be scalable, atomic multicast needs to be genuine, meaning that only the destination processes of a message should participate in ordering it. In this paper we propose a novel genuine atomic multicast protocol that in the absence of failures takes as low as 3 message delays to deliver a message when no other messages are multicast concurrently to its destination groups, and 5 message delays in the presence of concurrency. This improves the latencies of both the fault-tolerant version of classical Skeen's multicast protocol (6 or 12 message delays, depending on concurrency) and its recent improvement by Coelho et al. (4 or 8 message delays). To achieve such low latencies, we depart from the typical way of guaranteeing fault-tolerance by replicating each group with Paxos. Instead, we weave Paxos and Skeen's protocol together into a single coherent protocol, exploiting opportunities for white-box optimisations. We experimentally demonstrate that the superior theoretical characteristics of our protocol are reflected in practical performance pay-offs.
2020-02-17
Leite, Leonardo H. M., do Couto Boaventura, Wallace, de Errico, Luciano, Machado Alessi, Pedro.  2019.  Self-Healing in Distribution Grids Supported by Photovoltaic Dispersed Generation in a Voltage Regulation Perspective. 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America). :1–6.
Distributed Generation Photovoltaic Systems -DGPV - connected to the power distribution grid through electronic inverters can contribute, in an aggregate scenario, to the performance of several power system control functions, notably in self-healing and voltage regulation along a distribution feeder. This paper proposes the use of an optimization method for voltage regulation, focused on reactive power injection control, based on a comprehensive architecture model that coordinates multiple photovoltaic distributed sources to support grid reconfiguration after self-healing action. A sensitivity analysis regarding the performance of voltage regulation, based on a co-simulation of PSCAD and MatLab, shows the effectiveness of using dispersed generation sources to assist grid reconfiguration after disturbances caused by severe faults.
Broomandi, Fateme, Ghasemi, Abdorasoul.  2019.  An Improved Cooperative Cell Outage Detection in Self-Healing Het Nets Using Optimal Cooperative Range. 2019 27th Iranian Conference on Electrical Engineering (ICEE). :1956–1960.
Heterogeneous Networks (Het Nets) are introduced to fulfill the increasing demands of wireless communications. To be manageable, it is expected that these networks are self-organized and in particular, self-healing to detect and relief faults autonomously. In the Cooperative Cell Outage Detection (COD), the Macro-Base Station (MBS) and a group of Femto-Base Stations (FBSs) in a specific range are cooperatively communicating to find out if each FBS is working properly or not. In this paper, we discuss the impacts of the cooperation range on the detection delay and accuracy and then conclude that there is an optimal amount for cooperation range which maximizes detection accuracy. We then derive the optimal cooperative range that improves the detection accuracy by using network parameters such as FBS's transmission power, noise power, shadowing fading factor, and path-loss exponent and investigate the impacts of these parameters on the optimal cooperative range. The simulation results show the optimal cooperative range that we proposed maximizes the detection accuracy.
2020-01-27
Xue, Hong, Wang, Jingxuan, Zhang, Miao, Wu, Yue.  2019.  Emergency Severity Assessment Method for Cluster Supply Chain Based on Cloud Fuzzy Clustering Algorithm. 2019 Chinese Control Conference (CCC). :7108–7114.

Aiming at the composite uncertainty characteristics and high-dimensional data stream characteristics of the evaluation index with both ambiguity and randomness, this paper proposes a emergency severity assessment method for cluster supply chain based on cloud fuzzy clustering algorithm. The summary cloud model generation algorithm is created. And the multi-data fusion method is applied to the cloud model processing of the evaluation indexes for high-dimensional data stream with ambiguity and randomness. The synopsis data of the emergency severity assessment indexes are extracted. Based on time attenuation model and sliding window model, the data stream fuzzy clustering algorithm for emergency severity assessment is established. The evaluation results are rationally optimized according to the generalized Euclidean distances of the cluster centers and cluster microcluster weights, and the severity grade of cluster supply chain emergency is dynamically evaluated. The experimental results show that the proposed algorithm improves the clustering accuracy and reduces the operation time, as well as can provide more accurate theoretical support for the early warning decision of cluster supply chain emergency.

2020-01-20
Wu, Di, Chen, Tianen, Chen, Chienfu, Ahia, Oghenefego, Miguel, Joshua San, Lipasti, Mikko, Kim, Younghyun.  2019.  SECO: A Scalable Accuracy Approximate Exponential Function Via Cross-Layer Optimization. 2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED). :1–6.

From signal processing to emerging deep neural networks, a range of applications exhibit intrinsic error resilience. For such applications, approximate computing opens up new possibilities for energy-efficient computing by producing slightly inaccurate results using greatly simplified hardware. Adopting this approach, a variety of basic arithmetic units, such as adders and multipliers, have been effectively redesigned to generate approximate results for many error-resilient applications.In this work, we propose SECO, an approximate exponential function unit (EFU). Exponentiation is a key operation in many signal processing applications and more importantly in spiking neuron models, but its energy-efficient implementation has been inadequately explored. We also introduce a cross-layer design method for SECO to optimize the energy-accuracy trade-off. At the algorithm level, SECO offers runtime scaling between energy efficiency and accuracy based on approximate Taylor expansion, where the error is minimized by optimizing parameters using discrete gradient descent at design time. At the circuit level, our error analysis method efficiently explores the design space to select the energy-accuracy-optimal approximate multiplier at design time. In tandem, the cross-layer design and runtime optimization method are able to generate energy-efficient and accurate approximate EFU designs that are up to 99.7% accurate at a power consumption of 3.73 pJ per exponential operation. SECO is also evaluated on the adaptive exponential integrate-and-fire neuron model, yielding only 0.002% timing error and 0.067% value error compared to the precise neuron model.

Waqar, Ali, Hu, Junjie, Mushtaq, Muhammad Rizwan, Hussain, Hadi, Qazi, Hassaan Aziz.  2019.  Energy Management in an Islanded Microgrid: A Consensus Theory Approach. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). :1–6.

This article presents a consensus based distributed energy management optimization algorithm for an islanded microgrid. With the rapid development of renewable energy and distributed generation (DG) energy management is becoming more and more distributed. To solve this problem a multi-agent system based distributed solution is designed in this work which uses lambda-iteration method to solve optimization problem. Moreover, the algorithm is fully distributed and transmission losses are also considered in the modeling process which enhanced the practicality of proposed work. Simulations are performed for different cases on 8-bus microgrid to show the effectiveness of algorithm. Moreover, a scalability test is performed at the end to further justify the expandability performance of algorithm for more advanced networks.

2020-01-06
Fan, Zexuan, Xu, Xiaolong.  2019.  APDPk-Means: A New Differential Privacy Clustering Algorithm Based on Arithmetic Progression Privacy Budget Allocation. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :1737–1742.
How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering algorithms focus on optimizing the selection of initial centroids. However, the traditional privacy budget allocation has the problem that the random noise becomes too large as the number of iterations increases, which will reduce the performance of data clustering. To solve the problem, we improved the way of privacy budget allocation in differentially private clustering algorithm DPk-means, and proposed APDPk-means, a new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation. APDPk-means decomposes the total privacy budget into a decreasing arithmetic progression, allocating the privacy budgets from large to small in the iterative process, so as to ensure the rapid convergence in early iteration. The experiment results show that compared with the other differentially private k-means algorithms, APDPk-means has better performance in availability and quality of the clustering result under the same level of privacy protection.
2019-12-30
Shirasaki, Yusuke, Takyu, Osamu, Fujii, Takeo, Ohtsuki, Tomoaki, Sasamori, Fumihito, Handa, Shiro.  2018.  Consideration of security for PLNC with untrusted relay in game theoretic perspective. 2018 IEEE Radio and Wireless Symposium (RWS). :109–112.
A physical layer network coding (PLNC) is a highly efficient scheme for exchanging information between two nodes. Since the relay receives the interfered signal between two signals sent by two nodes, it hardly decodes any information from received signal. Therefore, the secure wireless communication link to the untrusted relay is constructed. The two nodes optimize the transmit power control for maximizing the secure capacity but these depend on the channel state information informed by the relay station. Therefore, the untrusted relay disguises the informed CSI for exploiting the information from two nodes. This paper constructs the game of two optimizations between the legitimate two nodes and the untrusted relay for clarifying the security of PLNC with untrusted relay.
2019-12-02
Simon, Laurent, Chisnall, David, Anderson, Ross.  2018.  What You Get is What You C: Controlling Side Effects in Mainstream C Compilers. 2018 IEEE European Symposium on Security and Privacy (EuroS P). :1–15.
Security engineers have been fighting with C compilers for years. A careful programmer would test for null pointer dereferencing or division by zero; but the compiler would fail to understand, and optimize the test away. Modern compilers now have dedicated options to mitigate this. But when a programmer tries to control side effects of code, such as to make a cryptographic algorithm execute in constant time, the problem remains. Programmers devise complex tricks to obscure their intentions, but compiler writers find ever smarter ways to optimize code. A compiler upgrade can suddenly and without warning open a timing channel in previously secure code. This arms race is pointless and has to stop. We argue that we must stop fighting the compiler, and instead make it our ally. As a starting point, we analyze the ways in which compiler optimization breaks implicit properties of crypto code; and add guarantees for two of these properties in Clang/LLVM. Our work explores what is actually involved in controlling side effects on modern CPUs with a standard toolchain. Similar techniques can and should be applied to other security properties; achieving intentions by compiler commands or annotations makes them explicit, so we can reason about them. It is already understood that explicitness is essential for cryptographic protocol security and for compiler performance; it is essential for language security too. We therefore argue that this should be only the first step in a sustained engineering effort.
2019-11-25
Guo, Tao, Yeung, Raymond w..  2018.  The Explicit Coding Rate Region of Symmetric Multilevel Diversity Coding. 2018 Information Theory and Applications Workshop (ITA). :1–9.
It is well known that superposition coding, namely separately encoding the independent sources, is optimal for symmetric multilevel diversity coding (SMDC) (Yeung-Zhang 1999). However, the characterization of the coding rate region therein involves uncountably many linear inequalities and the constant term (i.e., the lower bound) in each inequality is given in terms of the solution of a linear optimization problem. Thus this implicit characterization of the coding rate region does not enable the determination of the achievability of a given rate tuple. In this paper, we first obtain closed-form expressions of these uncountably many inequalities. Then we identify a finite subset of inequalities that is sufficient for characterizing the coding rate region. This gives an explicit characterization of the coding rate region. We further show by the symmetry of the problem that only a much smaller subset of this finite set of inequalities needs to be verified in determining the achievability of a given rate tuple. Yet, the cardinality of this smaller set grows at least exponentially fast with L.
2019-10-08
Arslan, B., Ulker, M., Akleylek, S., Sagiroglu, S..  2018.  A Study on the Use of Quantum Computers, Risk Assessment and Security Problems. 2018 6th International Symposium on Digital Forensic and Security (ISDFS). :1–6.

In the computer based solutions of the problems in today's world; if the problem has a high complexity value, different requirements can be addressed such as necessity of simultaneous operation of many computers, the long processing times for the operation of algorithms, and computers with hardware features that can provide high performance. For this reason, it is inevitable to use a computer based on quantum physics in the near future in order to make today's cryptosystems unsafe, search the servers and other information storage centers on internet very quickly, solve optimization problems in the NP-hard category with a very wide solution space and analyze information on large-scale data processing and to process high-resolution image for artificial intelligence applications. In this study, an examination of quantum approaches and quantum computers, which will be widely used in the near future, was carried out and the areas in which such innovation can be used was evaluated. Malicious or non-malicious use of quantum computers with this capacity, the advantages and disadvantages of the high performance which it provides were examined under the head of security, the effect of this recent technology on the existing security systems was investigated.

2019-06-24
Chouikhi, S., Merghem-Boulahia, L., Esseghir, M..  2018.  Energy Demand Scheduling Based on Game Theory for Microgrids. 2018 IEEE International Conference on Communications (ICC). :1–6.

The advent of smart grids offers us the opportunity to better manage the electricity grids. One of the most interesting challenges in the modern grids is the consumer demand management. Indeed, the development in Information and Communication Technologies (ICTs) encourages the development of demand-side management systems. In this paper, we propose a distributed energy demand scheduling approach that uses minimal interactions between consumers to optimize the energy demand. We formulate the consumption scheduling as a constrained optimization problem and use game theory to solve this problem. On one hand, the proposed approach aims to reduce the total energy cost of a building's consumers. This imposes the cooperation between all the consumers to achieve the collective goal. On the other hand, the privacy of each user must be protected, which means that our distributed approach must operate with a minimal information exchange. The performance evaluation shows that the proposed approach reduces the total energy cost, each consumer's individual cost, as well as the peak to average ratio.

2019-03-25
Hasan, K., Shetty, S., Hassanzadeh, A., Salem, M. B., Chen, J..  2018.  Self-Healing Cyber Resilient Framework for Software Defined Networking-Enabled Energy Delivery System. 2018 IEEE Conference on Control Technology and Applications (CCTA). :1692–1697.
Software defined networking (SDN) is a networking paradigm to provide automated network management at run time through network orchestration and virtualization. SDN can also enhance system resilience through recovery from failures and maintaining critical operations during cyber attacks. SDN's self-healing mechanisms can be leveraged to realized autonomous attack containment, which dynamically modifies access control rules based on configurable trust levels. In this paper, we present an approach to aid in selection of security countermeasures dynamically in an SDN enabled Energy Delivery System (EDS) and achieving tradeoff between providing security and QoS. We present the modeling of security cost based on end-to-end packet delay and throughput. We propose a non-dominated sorting based multi-objective optimization framework which can be implemented within an SDN controller to address the joint problem of optimizing between security and QoS parameters by alleviating time complexity at O(M N2), where M is the number of objective functions and N is the number of population for each generation respectively. We present simulation results which illustrate how data availability and data integrity can be achieved while maintaining QoS constraints.
2019-03-11
Li, Z., Xie, X., Ma, X., Guan, Z..  2018.  Trustworthiness Optimization of Industrial Cluster Network Platform Based on Blockchain. 2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS). :1–6.

Industrial cluster is an important organization form and carrier of development of small and medium-sized enterprises, and information service platform is an important facility of industrial cluster. Improving the credibility of the network platform is conducive to eliminate the adverse effects of distrust and information asymmetry on industrial clusters. The decentralization, transparency, openness, and intangibility of block chain technology make it an inevitable choice for trustworthiness optimization of industrial cluster network platform. This paper first studied on trusted standard of industry cluster network platform and construct a new trusted framework of industry cluster network platform. Then the paper focus on trustworthiness optimization of data layer and application layer of the platform. The purpose of this paper is to build an industrial cluster network platform with data access, information trustworthiness, function availability, high-speed and low consumption, and promote the sustainable and efficient development of industrial cluster.

2019-03-06
Li, W., Li, S., Zhang, X., Pan, Q..  2018.  Optimization Algorithm Research of Logistics Distribution Path Based on the Deep Belief Network. 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). :60-63.

Aiming at the phenomenon that the urban traffic is complex at present, the optimization algorithm of the traditional logistic distribution path isn't sensitive to the change of road condition without strong application in the actual logistics distribution, the optimization algorithm research of logistics distribution path based on the deep belief network is raised. Firstly, build the traffic forecast model based on the deep belief network, complete the model training and conduct the verification by learning lots of traffic data. On such basis, combine the predicated road condition with the traffic network to build the time-share traffic network, amend the access set and the pheromone variable of ant algorithm in accordance with the time-share traffic network, and raise the optimization algorithm of logistics distribution path based on the traffic forecasting. Finally, verify the superiority and application value of the algorithm in the actual distribution through the optimization algorithm contrast test with other logistics distribution paths.

Lin, Y., Liu, H., Xie, G., Zhang, Y..  2018.  Time Series Forecasting by Evolving Deep Belief Network with Negative Correlation Search. 2018 Chinese Automation Congress (CAC). :3839-3843.

The recently developed deep belief network (DBN) has been shown to be an effective methodology for solving time series forecasting problems. However, the performance of DBN is seriously depended on the reasonable setting of hyperparameters. At present, random search, grid search and Bayesian optimization are the most common methods of hyperparameters optimization. As an alternative, a state-of-the-art derivative-free optimizer-negative correlation search (NCS) is adopted in this paper to decide the sizes of DBN and learning rates during the training processes. A comparative analysis is performed between the proposed method and other popular techniques in the time series forecasting experiment based on two types of time series datasets. Experiment results statistically affirm the efficiency of the proposed model to obtain better prediction results compared with conventional neural network models.

2019-01-21
Feng, S., Xiong, Z., Niyato, D., Wang, P., Leshem, A..  2018.  Evolving Risk Management Against Advanced Persistent Threats in Fog Computing. 2018 IEEE 7th International Conference on Cloud Networking (CloudNet). :1–6.
With the capability of support mobile computing demand with small delay, fog computing has gained tremendous popularity. Nevertheless, its highly virtualized environment is vulnerable to cyber attacks such as emerging Advanced Persistent Threats attack. In this paper, we propose a novel approach of cyber risk management for the fog computing platform. Particularly, we adopt the cyber-insurance as a tool for neutralizing cyber risks from fog computing platform. We consider a fog computing platform containing a group of fog nodes. The platform is composed of three main entities, i.e., the fog computing provider, attacker, and cyber-insurer. The fog computing provider dynamically optimizes the allocation of its defense computing resources to improve the security of the fog computing platform. Meanwhile, the attacker dynamically adjusts the allocation of its attack resources to improve the probability of successful attack. Additionally, to prevent from the potential loss due to attacks, the provider also makes a dynamic decision on the purchases ratio of cyber-insurance from the cyber-insurer for each fog node. Thereafter, the cyber-insurer accordingly determines the premium of cyber-insurance for each fog node. In our formulated dynamic Stackelberg game, the attacker and provider act as the followers, and the cyber-insurer acts as the leader. In the lower level, we formulate an evolutionary subgame to analyze the provider's defense and cyber-insurance subscription strategies as well as the attacker's attack strategy. In the upper level, the cyber-insurer optimizes its premium determination strategy, taking into account the evolutionary equilibrium at the lower-level evolutionary subgame. We analytically prove that the evolutionary equilibrium is unique and stable. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the dynamic Stackelberg game.
2019-01-16
Carlini, N., Wagner, D..  2018.  Audio Adversarial Examples: Targeted Attacks on Speech-to-Text. 2018 IEEE Security and Privacy Workshops (SPW). :1–7.
We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (recognizing up to 50 characters per second of audio). We apply our white-box iterative optimization-based attack to Mozilla's implementation DeepSpeech end-to-end, and show it has a 100% success rate. The feasibility of this attack introduce a new domain to study adversarial examples.
2018-12-10
Murray, B., Islam, M. A., Pinar, A. J., Havens, T. C., Anderson, D. T., Scott, G..  2018.  Explainable AI for Understanding Decisions and Data-Driven Optimization of the Choquet Integral. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.
To date, numerous ways have been created to learn a fusion solution from data. However, a gap exists in terms of understanding the quality of what was learned and how trustworthy the fusion is for future-i.e., new-data. In part, the current paper is driven by the demand for so-called explainable AI (XAI). Herein, we discuss methods for XAI of the Choquet integral (ChI), a parametric nonlinear aggregation function. Specifically, we review existing indices, and we introduce new data-centric XAI tools. These various XAI-ChI methods are explored in the context of fusing a set of heterogeneous deep convolutional neural networks for remote sensing.
Chen, J., Touati, C., Zhu, Q..  2017.  Heterogeneous Multi-Layer Adversarial Network Design for the IoT-Enabled Infrastructures. GLOBECOM 2017 - 2017 IEEE Global Communications Conference. :1–6.

The emerging Internet of Things (IoT) applications that leverage ubiquitous connectivity and big data are facilitating the realization of smart everything initiatives. IoT-enabled infrastructures have naturally a multi-layer system architecture with an overlaid or underlaid device network and its coexisting infrastructure network. The connectivity between different components in these two heterogeneous networks plays an important role in delivering real-time information and ensuring a high-level situational awareness. However, IoT- enabled infrastructures face cyber threats due to the wireless nature of communications. Therefore, maintaining the network connectivity in the presence of adversaries is a critical task for the infrastructure network operators. In this paper, we establish a three-player three-stage game-theoretic framework including two network operators and one attacker to capture the secure design of multi- layer infrastructure networks by allocating limited resources. We use subgame perfect Nash equilibrium (SPE) to characterize the strategies of players with sequential moves. In addition, we assess the efficiency of the equilibrium network by comparing with its team optimal solution counterparts in which two network operators can coordinate. We further design a scalable algorithm to guide the construction of the equilibrium IoT-enabled infrastructure networks. Finally, we use case studies on the emerging paradigm of Internet of Battlefield Things (IoBT) to corroborate the obtained results.

Farooq, M. J., Zhu, Q..  2018.  On the Secure and Reconfigurable Multi-Layer Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT). IEEE Transactions on Wireless Communications. 17:2618–2632.

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas, such as smart homes, smart cities, health care, and transportation. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to battlefield specific challenges, such as the absence of communication infrastructure, heterogeneity of devices, and susceptibility to cyber-physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and information dissemination in the presence of adversaries. This paper aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to quantify the information dissemination among heterogeneous network devices. Consequently, a tractable optimization problem is formulated that can assist commanders in cost effectively planning the network and reconfiguring it according to the changing mission requirements.

2018-12-03
Matta, R. de, Miller, T..  2018.  A Strategic Manufacturing Capacity and Supply Chain Network Design Contingency Planning Approach. 2018 IEEE Technology and Engineering Management Conference (TEMSCON). :1–6.

We develop a contingency planning methodology for how a firm would build a global supply chain network with reserve manufacturing capacity which can be strategically deployed by the firm in the event actual demand exceeds forecast. The contingency planning approach is comprised of: (1) a strategic network design model for finding the profit maximizing plant locations, manufacturing capacity and inventory investments, and production level and product distribution; and (2) a scenario planning and risk assessment scheme to analyze the costs and benefits of alternative levels of manufacturing capacity and inventory investments. We develop an efficient heuristic procedure to solve the model. We show numerically how a firm would use our approach to explore and weigh the potential upside benefits and downside risks of alternative strategies.

2018-11-19
Chen, D., Liao, J., Yuan, L., Yu, N., Hua, G..  2017.  Coherent Online Video Style Transfer. 2017 IEEE International Conference on Computer Vision (ICCV). :1114–1123.
Training a feed-forward network for the fast neural style transfer of images has proven successful, but the naive extension of processing videos frame by frame is prone to producing flickering results. We propose the first end-to-end network for online video style transfer, which generates temporally coherent stylized video sequences in near realtime. Two key ideas include an efficient network by incorporating short-term coherence, and propagating short-term coherence to long-term, which ensures consistency over a longer period of time. Our network can incorporate different image stylization networks and clearly outperforms the per-frame baseline both qualitatively and quantitatively. Moreover, it can achieve visually comparable coherence to optimization-based video style transfer, but is three orders of magnitude faster.
Huang, X., Belongie, S..  2017.  Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization. 2017 IEEE International Conference on Computer Vision (ICCV). :1510–1519.
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.