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Automation in road vehicles is an emerging technology that has developed rapidly over the last decade. There have been many inter-disciplinary challenges posed on existing transportation infrastructure by autonomous vehicles. In this paper, we conduct an algorithmic study on when and how an autonomous vehicle should change its lane, which is a fundamental problem in vehicle automation field and root cause of most ‘phantom’ traffic jams. We propose a prediction-and-decision framework, called Cheetah (Change lane smart for autonomous vehicle), which aims to optimize the lane changing maneuvers of autonomous vehicle while minimizing its impact on surrounding vehicles. In the prediction phase, Cheetah learns the spatio-temporal dynamics from historical trajectories of surrounding vehicles with a deep model (GAS-LED model) and predict their corresponding actions in the near future. A global attention mechanism and state sharing strategy are also incorporated to achieve higher accuracy and better convergence efficiency. Then in the decision phase, Cheetah looks for optimal lane change maneuvers for the autonomous vehicle by taking into account a few factors such as speed, impact on other vehicles and safety issues. A tree-based adaptive beam search algorithm is designed to reduce the search space and improve accuracy. In order to make our framework applicable to more scenarios, we further propose an improved Cheetah (Cheetah+) framework that makes the autonomous vehicle adapt for exiting a road and meet the requirement for driving comfort. Extensive experiments offer evidence that the proposed framework can advance the state of the art in terms of effectiveness and efficiency. IEEE

In recent years, the potential applications of Large Multimodal Models (LMMs) in fields such as healthcare, social psychology, and industrial design have attracted wide research attention, providing new directions for human factors research. For instance, LMM-based smart systems have become novel research subjects of human factors studies, and LMM introduces new research paradigms and methodologies to this field. Therefore, this paper aims to explore the applications, challenges, and future prospects of LMM in the domain of human factors and ergonomics through an expert-LMM collaborated literature review. Specifically, a novel literature review method is proposed, and research studies of LMM-based accident analysis, human modelling and intervention design are introduced. Subsequently, the paper discusses future trends of the research paradigm and challenges of human factors and ergonomics studies in the era of LMMs. It is expected that this study can provide a valuable perspective and serve as a reference for integrating human factors with artificial intelligence., Comment: 14 pages, journal paper

BACKGROUND: Inability to return to work (RTW) is common after acute respiratory distress syndrome (ARDS). OBJECTIVES: The aim of this study is to examine interrelationships among pre-ARDS workload, illness severity, and post-ARDS cognitive, psychological, interpersonal, and physical function with RTW at 6 and 12 months after ARDS. METHODS: We conducted a secondary analysis using the US multicentre ARDS Network Long-Term Outcomes Study. The US Occupational Information Network was used to determine pre-ARDS workload. The Mini-Mental State Examination and SF-36 were used to measure four domains of post-ARDS function. Analyses used structural equation modeling and mediation analyses. RESULTS: Among 329 previously employed ARDS survivors, 6- and 12-month RTW rates were 52% and 56%, respectively. Illness severity (standardised coefficients range: -0.51 to -0.54, p < 0.001) had a negative effect on RTW at 6 months, whereas function at 6 months (psychological [0.42, p < 0.001], interpersonal [0.40, p < 0.001], and physical [0.43, p < 0.001]) had a positive effect. Working at 6 months (0.79 to 0.72, P < 0.001) had a positive effect on RTW at 12 months, whereas illness severity (-0.32 to -0.33, p = 0.001) and post-ARDS function (psychological [6 months: 0.44, p < 0.001; 12 months: 0.33, p = 0.002], interpersonal [0.44, p < 0.001; 0.22, p = 0.03], and physical abilities [0.47, p < 0.001; 0.33, p = 0.007]) only had an indirect effect on RTW at 12 months mediated through work at 6 months. CONCLUSIONS: RTW at 12 months was associated with patients illness severity; post-ARDS cognitive, psychological, interpersonal, and physical function; and working at 6 months. Among these factors, working at 6 months and function may be modifiable mediators of 12-month post-ARDS RTW. Improving ARDS survivors RTW may include optimisation o

We present a new proof rule for verifying lower bounds on quantities of probabilistic programs. Our proof rule is not confined to almost-surely terminating programs -- as is the case for existing rules -- and can be used to establish non-trivial lower bounds on, e.g., termination probabilities and expected values, for possibly divergent probabilistic loops, e.g., the well-known three-dimensional random walk on a lattice., Comment: Paper conditionally accepted by OOPSLA 2023

Autonomous driving is an emerging technology that has advanced rapidly over the last decade. Modern transportation is expected to benefit greatly from a wise decision-making framework of autonomous vehicles, including the improvement of mobility and the minimization of risks and travel time. However, existing methods either ignore the complexity of environments only fitting straight roads, or ignore the impact on surrounding vehicles during optimization phases, leading to weak environmental adaptability and incomplete optimization objectives. To address these limitations, we propose a parameterized decision-making framework with multi-modal perception based on deep reinforcement learning, called AUTO. We conduct a comprehensive perception to capture the state features of various traffic participants around the autonomous vehicle, based on which we design a graph-based model to learn a state representation of the multi-modal semantic features. To distinguish between lane-following and lane-changing, we decompose an action of the autonomous vehicle into a parameterized action structure that first decides whether to change lanes and then computes an exact action to execute. A hybrid reward function takes into account aspects of safety, traffic efficiency, passenger comfort, and impact to guide the framework to generate optimal actions. In addition, we design a regularization term and a multi-worker paradigm to enhance the training. Extensive experiments offer evidence that AUTO can advance state-of-the-art in terms of both macroscopic and microscopic effectiveness., Comment: IEEE International Conference on Data Engineering (ICDE2024)

Controller synthesis, including reset controller, feedback controller, and switching logic controller, provides an essential mechanism to guarantee the correctness and reliability of hybrid systems in a correct-by-construction manner. Unfortunately, reset controller synthesis is still in an infant stage in the literature, although it makes theoretical and practical significance. In this paper, we propose a convex programming based method to synthesize reset controllers for polynomial hybrid systems subject to safety, possibly together with liveness. Such a problem essentially corresponds to computing an initial set of continuous states in each mode and a reset map associated with each discrete jump such that any trajectory starting from any computed initial state keeps safe if only safety constraints are given or reaches the target set eventually and keeps safe before that if both safety and liveness are given, through the computed reset maps. Both cases can be reduced to reach-avoid and/or differential invariant generation problems, further encoded as convex optimization problems. Finally, several examples are provided to demonstrate the efficiency and effectiveness of our method., Comment: 26 pages, 8 figures

A reset controller plays a crucial role in designing hybrid systems. It restricts the initial set and redefines the reset map associated with discrete transitions, in order to guarantee the system to achieve its objective. Reset controller synthesis, together with feedback controller synthesis and switching logic controller synthesis, provides a correct-by-construction approach to designing hybrid systems. However, time-delay is an inevitable factor in hybrid systems, which can degrade control performance and render verification certificates obtained by abstracting away time-delay invalid in practice. In this paper, we investigate this issue in a practical manner by taking time-delay into account. We propose an approach that reduces the synthesis of reset controllers to the generation of reach-avoid sets for the hybrid system under consideration, which can be efficiently solved using off-the-shell convex optimization solvers., Comment: 15 pages, 10 figures

BACKGROUND: Joblessness is common after ARDS, but related risk factors are not fully understood. RESEARCH QUESTION: What is the association between survivors pre-ARDS workload and post-ARDS functional impairment, pain, and fatigue with their return to work (RTW) status? STUDY DESIGN AND METHODS: The U.S. Occupational Information Network (O∗NET) was used to determine pre-ARDS workload for participants in the ARDS Network Long-Term Outcomes Study (ALTOS). Post-ARDS functional impairment was assessed using the Mini-Mental State Examination and SF-36 Physical Functioning, Social Functioning, and Mental Health sub-scales, and categorized as either no impairments, only psychosocial impairment, physical with low psychosocial impairment, or physical with high psychosocial impairment. Post-ARDS pain and fatigue were assessed using the SF-36 pain item and Functional Assessment of Chronic Illness Therapy-Fatigue Scale fatigue scale, respectively. Generalized linear mixed modeling methods were used to evaluate associations among pre-ARDS workload, post-ARDS functional impairment, and symptoms of pain and fatigue with post-ARDS RTW. RESULTS: Pre-ARDS workload was not associated with post-ARDS RTW. However, as compared with survivors with no functional impairment, those with only psychosocial impairment (OR [CI]: 0.18 [0.06-0.50]), as well as physical impairment plus either low psychosocial impairment (0.08 [0.03-0.22]) or high psychosocial impairment (0.01 [0.003-0.05]) had lower odds of working. Pain (0.06 [0.03-0.14]) and fatigue (0.07 [0.03-0.16]) were also negatively associated with RTW. INTERPRETATION: For previously employed survivors of ARDS, post-ARDS psychosocial and physical impairments, pain, and fatigue were negatively associated with RTW, whereas pre-ARDS workload was not associated. These findings are important for designing and implementing vocational interventions for ARDS survivors.

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Porous asphalt mixture can be used as a road surface paving material with the remarkable advantage to prevent water accumulation and ponding. However, the performance of porous asphalt mixture in low temperature environment has not been thoroughly investigated, and this forms the subject of research in the present study. The mineral aggregate gradation of porous asphalt mixture was designed based on Bailey method, and the low temperature performance of porous asphalt mixture was studied by means of the low temperature bending test. The factors affecting the low temperature performance of porous asphalt mixture were analyzed through the orthogonal experimental design method, and the effects of porosity, modifier content, aging condition, and test temperature on the low temperature performance of porous asphalt mixture were evaluated. The results showed that the modifier content was the most important factor affecting the low temperature performance of porous asphalt mixture, followed by the test temperature, while the porosity and the aging condition were the least. Among the three performance evaluation indicators, namely the flexural tensile strength, maximum bending strain, and bending stiffness modulus, the maximum bending strain had the highest sensitivity to the porosity. It can be seen from the single factor influence test of porosity that there existed an approximately linear relationship between the maximum bending strain and the porosity of porous asphalt mixture, and the maximum bending strain decreased with increasing porosity. Furthermore, in order to ensure the good working performance of porous asphalt mixture in low temperature environment, the porosity should also satisfy the required limits of the maximum bending strain.

Giant panda (panda) is a highly endangered animal. Significant efforts and resources have been put on panda conservation. To measure effectiveness of conservation schemes, estimating its population size in wild is an important task. The current population estimation approaches, including capture-recapture, human visual identification and collection of DNA from hair or feces, are invasive, subjective, costly or even dangerous to the workers who perform these tasks in wild. Cameras have been widely installed in the regions where pandas live. It opens a new possibility for non-invasive image based panda recognition. Panda face recognition is naturally a small dataset problem, because of the number of pandas in the world and the number of qualified images captured by the cameras in each encounter. In this paper, a panda face recognition algorithm, which includes alignment, large feature set extraction and matching is proposed and evaluated on a dataset consisting of 163 images. The experimental results are encouraging., Comment: Accepted in the IEEE 2019 International Conference on Image Processing (ICIP 2019), scheduled for 22-25 September 2019 in Taipei, Taiwan

Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This study focuses on how to improve the accuracy and efficiency of predicting citywide human mobility via a simpler solution. A spatio-temporal mobility event prediction framework based on a single fully-convolutional residual network (STAR) is proposed. STAR is a highly simple, general and effective method for learning a single tensor representing the mobility event. Residual learning is utilized for training the deep network to derive the detailed result for scenarios of citywide prediction. Extensive benchmark evaluation results on real-world data demonstrate that STAR outperforms state-of-the-art approaches in single- and multi-step prediction while utilizing fewer parameters and achieving higher efficiency., Comment: Accepted by MDM 2019

With the advances in geo-positioning technologies and location-based services, it is nowadays quite common for road networks to have textual contents on the vertices. Previous work on identifying an optimal route that covers a sequence of query keywords has been studied in recent years. However, in many practical scenarios, an optimal route might not always be desirable. Therefore, in this paper, we investigate the problem of clue-based route search (CRS), which allows a user to provide clues on keywords and spatial relationships. First, we propose a greedy algorithm and a dynamic programming algorithm as baselines. To improve efficiency, we develop a branch-And-bound algorithm that prunes unnecessary vertices in query processing. In order to quickly locate candidate, we propose an AB-Tree that stores both the distance and keyword information in tree structure. To further reduce the index size, we construct a PB-Tree by utilizing the virtue of 2-hop label index to pinpoint the candidate. Extensive experiments are conducted and verify the superiority of our algorithms and index structures. © 2018 IEEE.

It is nowadays quite common for road networks to have textual contents on the vertices, which describe auxiliary information (e.g., business, traffic, etc.) associated with the vertex. In such road networks, which are modelled as weighted undirected graphs, each vertex is associated with one or more keywords, and each edge is assigned with a weight, which can be its physical length or travelling time. In this paper, we study the problem of keyword-aware continuous k nearest neighbour (KCkNN) search on road networks, which computes the k nearest vertices that contain the query keywords issued by a moving object and maintains the results continuously as the object is moving on the road network. Reducing the query processing costs in terms of computation and communication has attracted considerable attention in the database community with interesting techniques proposed. This paper proposes a framework, called a Labelling AppRoach for Continuous kNN query (LARC), on road networks to cope with KCkNN query efficiently. First we build a pivot-based reverse label index and a keyword-based pivot tree index to improve the efficiency of keyword-aware k nearest neighbour (KkNN) search by avoiding massive network traversals and sequential probe of keywords. To reduce the frequency of unnecessary result updates, we develop the concepts of dominance interval and region on road network, which share the similar intuition with safe region for processing continuous queries in Euclidean space but are more complicated and thus require more dedicated design. For high frequency keywords, we resolve the dominance interval when the query results changed. In addition, a path-based dominance updating approach is proposed to compute the dominance region efficiently when the query keywords are of low frequency. We conduct extensive experiments by comparing our algorithms with the state-of-the-art methods on real data sets. The empirical observations have verified the superiority of our propose

In order to reduce the isolation performance requirement of a duplexer in an FDD transceiver, this paper presents a TX leakage cancellation technique that is digitally controlled, operates at RF frequencies and is designed to suppress the in-band leakage with respect to the RX band. Not only the attenuation and phase, but also the delays are optimized by using the proposed MADF-DC estimation technique. In order to demonstrate the concept, the cancellation system with a ceramic duplexer is prototyped using dedicated components for the transceiver test bench that uses a ceramic duplexer. The design bandwidth of the system is 35 MHz at the centre frequency of 897.5 MHz. More than 11 dB and 20 dB of cancellation have been achieved by measurement and simulation, respectively.

Wideband interference signals appearing at 20 MHz, 15 MHz and even 10 MHz away from the edge of the mobile received signal can now be suppressed by more than 30 dB by following the new RF interference cancellation technique introduced in this paper using bandstop resonators. Furthermore, a new methodology for designing a 3-pole bandstop resonator with very sharp rejection is presented. The resonators are implemented using a power splitter and different number of cells from the composite right/left handed transmission (CRLH-T) line circuit model. Two resonators are fabricated and measured: using two cells; and three cells, showing an insertion loss of less than 1.2 dB. An interference cancellation test bench is set using two 9 MHz LTE modulated signals with equal average peak power (40 dBm). The power spectrum measurements show more than 30 dB cancellation for the interferer injected at 20 MHz separation from the desired signal using the two-cell resonator. More than 45 dB and 30 dB cancellation are also achieved for interferers with only 15 MHz and 10 MHz frequency separation using the three-cell resonator. The tests are repeated with high power interferers (e.g. 20 dB above the received signal level) and the same cancellation performance is maintained without retuning any of the components used.

A duplexer is necessary, but unfavourable for a frequency-division duplexing (FDD) base station, due to its bulky size, high cost and design challenges. In order to relax the performance requirement of such device, the transmitter (TX) leakage needs to be suppressed. The state-of-the-art solutions failed to provide a wideband solution for cancelling the TX leakage at RF frequency, due to the lack of delay optimization. Aiming to provide high delay estimation accuracy, this paper presents a modelling technique which is based on the compressed sampling matching pursuit (CoSaMP) algorithm for compressed sampling (CS). As a result, by using the proposed modelling technique, cancellation systems, particularly the ones that are based on the analog finite impulse response (FIR) filter structure, can be implemented to achieve wideband suppressing at RF frequencies.

Assuming there is an infinite periodically sinusoidal signal x with known frequency, one can sample x at Nyquist zone or sampling-rate to recover the signal. In terms of digital predistortion (DPD) with modulated signal as input, this paper will show the same concept that theoretically one can sample the feedback signal at arbitrary sampling rate to obtain the same coefficient of DPD or behavioural models as the one calculated from the high sampling rate feedback signal. The performance of a generalised DPD technique using a traditional memory polynomial with low-rate analog-to-digital device(ADC) is experimentally validated. The NMSE and ACPR results indicate the same performance by using an ADC sampling from 100 MHz to 10 MHz.

Su Han Lum, John D GraingerDepartment of Blood and Marrow Transplant, Royal Manchester Children’s Hospital, Manchester, UK Abstract: Aplastic anemia (AA) is a potential life-threatening hematopoietic stem cell (HSC) disorder resulting in cytopenia. The mainstays of treatment for AA are definitive therapy to restore HSCs and supportive measures to ameliorate cytopenia-related complications. The standard definitive therapy is HSC transplantation for young and medically fit patients with suitable donors and immunosuppressive therapy (IST) with antithymocyte globulin and cyclosporine for the remaining patients. A significant proportion of patients are refractory to IST or relapse after IST. Various strategies have been explored in these patients, including second course of antithymocyte globulin, high-dose cyclophosphamide, and alemtuzumab. Eltrombopag, a thrombopoietin mimetic, has recently emerged as an encouraging and promising agent for patients with refractory AA. It has demonstrated efficacy in restoring trilineage hematopoiesis, and this positive effect continues after discontinuation of the drug. There are ongoing clinical trials exploring the role of eltrombopag as a first-line therapy in moderate to severe AA and a combination of eltrombopag with IST in severe AA.Keywords: eltrombopag, aplastic anemia, thrombopoietin, c-Mpl receptors

QC 20151216

Route recommendation service has become a big business in industry since traveling is now an important part of our daily life. We can travel to unknown places by simply typing in our destination and then following recommendation service's guidance, that a pleasant trip desires them to provide a good route. However, previous research shows that even the routes recommended by the big-thumb service providers can deviate significantly from the routes travelled by experienced drivers since the many latent factors affect drivers' preferences and it is hard for a single route recommendation algorithm to model all of them. In this demo we will present the CrowPlanner system to leverage crowds' knowledge to improve the recommendation quality. It requests human workers to evaluate candidates routes recommended by different sources and methods, and determines the best route based on the feedbacks of these workers. In this demo, we first introduce the core component of our system for smart question generation, and then show several real route recommendation cases and the feedback of users. © 2014 IEEE.

As travel is taking more significant part in our life, route recommendation service becomes a big business and attracts many major players in IT industry. Given a pair of user-specified origin and destination, a route recommendation service aims to provide users with the routes of best travelling experience according to criteria, such as travelling distance, travelling time, traffic condition, etc. However, previous research shows that even the routes recommended by the big-thumb service providers can deviate significantly from the routes travelled by experienced drivers. It means travellers' preferences on route selection are influenced by many latent and dynamic factors that are hard to model exactly with pre-defined formulas. In this work we approach this challenging problem with a very different perspective- leveraging crowds' knowledge to improve the recommendation quality. In this light, CrowdPlanner - a novel crowd-based route recommendation system has been developed, which requests human workers to evaluate candidate routes recommended by different sources and methods, and determine the best route based on their feedbacks. In this paper, we particularly focus on two important issues that affect system performance significantly: (1) how to efficiently generate tasks which are simple to answer but possess sufficient information to derive user-preferred routes; and (2) how to quickly identify a set of appropriate domain experts to answer the questions timely and accurately. Specifically, the task generation component in our system generates a series of informative and concise questions with optimized ordering for a given candidate route set so that workers feel comfortable and easy to answer. In addition, the worker selection component utilizes a set of selection criteria and an efficient algorithm to find the most eligible workers to answer the questions with high accuracy. A prototype system has been deployed to many voluntary mobile clients and extensive tes

In an Frequency-Division Duplexing (FDD) transceiver, a high power transmitted signal can leak into the RX chain due to limited isolation of a duplexer. Such signal is known as TX leakage and can seriously affect the performance of the receiver. A transmitter leakage canceller structure is proposed in this paper in order to improve isolation between the transmitter and receiver. The simulations show the proposed structure increases the isolation of a Surface Acoustic Wave (SAW) duplexer by more than 25 dB in 20 MHz bandwidth

Transmitter leakage is caused by limited isolation between Tx and Rx paths of an frequency-division duplexing (FDD) transceiver due to use of non-ideal duplexers. As the leakage can seriously impair the performance of the receiver, it is important to increase the isolation of the existing system. A portion of the leakage falling into the Rx band can't be removed using conventional band-pass techniques, which leads to exploration of active cancellation methods. This paper presents an active Tx leakage cancellation structure with adaptive delay filter, together with a related design method- ology. Based on S-parameters characterisation of a SAW duplexer, simulation results show that for 20 MHz bandwidth more than 25 dB cancellation can be achieved, for LTE band 8 carrier. The complexity of proposed cancellation structure is proportional to the characteristics of a duplexer used in the transceiver. Using finite number of elements, the number of auxiliary paths needed depends on how complex the characteristics of the duplexer are within the band of interest.

Due to the prevalence of GPS-enabled devices and wireless communications technologies, spatial trajectories that describe the movement history of moving objects are being generated and accumulated at an unprecedented pace. Trajectory data in a database are intrinsically heterogeneous, as they represent discrete approximations of original continuous paths derived using different sampling strategies and different sampling rates. Such heterogeneity can have a negative impact on the effectiveness of trajectory similarity measures, which are the basis of many crucial trajectory processing tasks. In this paper, we pioneer a systematic approach to trajectory calibration that is a process to transform a heterogeneous trajectory dataset to one with (almost) unified sampling strategies. Specifically, we propose an anchor-based calibration system that aligns trajectories to a set of anchor points, which are fixed locations independent of trajectory data. After examining four different types of anchor points for the purpose of building a stable reference system, we propose a geometry-based calibration approach that considers the spatial relationship between anchor points and trajectories. Then a more advanced model-based calibration method is presented, which exploits the power of machine learning techniques to train inference models from historical trajectory data to improve calibration effectiveness. Finally, we conduct extensive experiments using real trajectory datasets to demonstrate the effectiveness and efficiency of the proposed calibration system. Copyright © 2013 ACM.

The last decade has witnessed the prevalence of sensor and GPS technologies that produce a sheer volume of trajectory data representing the motion history of moving objects. Measuring similarity between trajectories is undoubtedly one of the most important tasks in trajectory data management since it serves as the foundation of many advanced analyses such as similarity search, clustering, and classification. In this light, tremendous efforts have been spent on this topic, which results in a large number of trajectory similarity measures. Generally, each individual work introducing a new distance measure has made specific claims on the superiority of their proposal. However, for most works, the experimental study was focused on demonstrating the efficiency of the search algorithms, leaving the effectiveness aspect unverified empirically. In this paper, we conduct a comparative experimental study on the effectiveness of six widely used trajectory similarity measures based on a real taxi trajectory dataset. By applying a variety of transformations we designed for each original trajectory, our experimental observations demonstrate the advantages and drawbacks of these similarity measures in different circumstances. © 2013, Australian Computer Society, Inc.

CrowdPlanner -- a novel crowd-based route recommendation system has been developed, which requests human workers to evaluate candidates routes recommended by different sources and methods, and determine the best route based on the feedbacks of these workers. Our system addresses two critical issues in its core components: a) task generation component generates a series of informative and concise questions with optimized ordering for a given candidate route set so that workers feel comfortable and easy to answer; and b) worker selection component utilizes a set of selection criteria and an efficient algorithm to find the most eligible workers to answer the questions with high accuracy.

Risk Limiting Dispatch (RLD) was proposed recently as a mechanism that utilizes information and market recourse to reduce reserve capacity requirements, emissions and achieve other system operator objectives. It induces a set of simple dispatch rules that can be easily embedded into the existing dispatch systems to provide computationally efficient and reliable decisions. Storage is emerging as an alternative to mitigate the uncertainty in the grid. This paper extends the RLD framework to incorporate fast-ramping storage. It developed a closed form threshold rule for the optimal stochastic dispatch incorporating a sequence of markets and real-time information. An efficient algorithm to evaluate the thresholds is developed based on analysis of the optimal storage operation. Simple approximations that rely on continuous-time approximations of the solution for the discrete time control problem are also studied. The benefits of storage with respect to prediction quality and storage capacity are examined, and the overall effect on dispatch is quantified. Numerical experiments illustrate the proposed procedures., Comment: Submitted to IEEE Transactions on Automatic Control

The high variability of renewable energy resources presents significant challenges to the operation of the electric power grid. Conventional generators can be used to mitigate this variability but are costly to operate and produce carbon emissions. Energy storage provides a more environmentally friendly alternative, but is costly to deploy in large amounts. This paper studies the limits on the benefits of energy storage to renewable energy: How effective is storage at mitigating the adverse effects of renewable energy variability? How much storage is needed? What are the optimal control policies for operating storage? To provide answers to these questions, we first formulate the power flow in a single-bus power system with storage as an infinite horizon stochastic program. We find the optimal policies for arbitrary net renewable generation process when the cost function is the average conventional generation (environmental cost) and when it is the average loss of load probability (reliability cost). We obtain more refined results by considering the multi-timescale operation of the power system. We view the power flow in each timescale as the superposition of a predicted (deterministic) component and an prediction error (residual) component and formulate the residual power flow problem as an infinite horizon dynamic program. Assuming that the net generation prediction error is an IID process, we quantify the asymptotic benefits of storage. With the additional assumption of Laplace distributed prediction error, we obtain closed form expressions for the stationary distribution of storage and conventional generation. Finally, we propose a two-threshold policy that trades off conventional generation saving with loss of load probability. We illustrate our results and corroborate the IID and Laplace assumptions numerically using datasets from CAISO and NREL., Comment: 45 pages, 17 figures

We investigate distributed source coding of two correlated sources X and Y where messages are passed to a decoder in a cascade fashion. The encoder of X sends a message at rate R_1 to the encoder of Y. The encoder of Y then sends a message to the decoder at rate R_2 based both on Y and on the message it received about X. The decoder's task is to estimate a function of X and Y. For example, we consider the minimum mean squared-error distortion when encoding the sum of jointly Gaussian random variables under these constraints. We also characterize the rates needed to reconstruct a function of X and Y losslessly. Our general contribution toward understanding the limits of the cascade multiterminal source coding network is in the form of inner and outer bounds on the achievable rate region for satisfying a distortion constraint for an arbitrary distortion function d(x,y,z). The inner bound makes use of a balance between two encoding tactics--relaying the information about X and recompressing the information about X jointly with Y. In the Gaussian case, a threshold is discovered for identifying which of the two extreme strategies optimizes the inner bound. Relaying outperforms recompressing the sum at the relay for some rate pairs if the variance of X is greater than the variance of Y., Comment: ISIT 2009, 5 pages, 1 eps figure, uses IEEEtran.cls

An information theoretic formulation of the distributed averaging problem previously studied in computer science and control is presented. We assume a network with m nodes each observing a WGN source. The nodes communicate and perform local processing with the goal of computing the average of the sources to within a prescribed mean squared error distortion. The network rate distortion function R^*(D) for a 2-node network with correlated Gaussian sources is established. A general cutset lower bound on R^*(D) is established and shown to be achievable to within a factor of 2 via a centralized protocol over a star network. A lower bound on the network rate distortion function for distributed weighted-sum protocols, which is larger in order than the cutset bound by a factor of log m is established. An upper bound on the network rate distortion function for gossip-base weighted-sum protocols, which is only log log m larger in order than the lower bound for a complete graph network, is established. The results suggest that using distributed protocols results in a factor of log m increase in order relative to centralized protocols., Comment: 25 pages, 1 figure

This thesis primarily aims to examine the causal relationships among the integration strategies of seaport terminals along the supply chain, and the antecedents and consequences of the integration strategies. In this thesis integration strategy is termed Port Supply Chain Integration (PSCI) and the antecedents and the consequences of PSCI were identified and termed as Port Supply Chain Orientation (PSCO) and Port Performance (PP). A research model representing these causal relationships was developed through a comprehensive literature review and a semi-structured interview with practitioners and academics. The research used Structural Equation Modelling (SEM) to validate the constructs and rigorously test the relationships among them. Furthermore, a multi-group analysis was conducted in order to assess the applicability of the research model to different environments. The multi-group analysis tested whether the research model can be applied to two sample ports (A and B) and two sample groups (terminal operating companies and port users). The data used in these empirical analyses were collected from terminal operating companies, shipping companies and freight forwarders in South Korea, and the number of responses was 127. With regard to the results and findings of the empirical research, firstly, the three constructs, PSCO, PSCI and PP were successfully validated with the components identified from the literature review and interviews indicating that the three constructs are multi-dimensional concepts. Secondly, the empirical research showed that PSCO has a strong contribution to PSCI, and PSCI has a strong and positive impact on PP. Additionally PSCO, in turn, was found to influence positively and indirectly on PP through implementing PSCI. Finally the multi-group analysis showed that the research model can be equally applied to the two sample ports. However, the invariance test across two sample groups failed since a baseline model for the invariance test was not e

This thesis primarily aims to examine the causal relationships among the integration strategies of seaport terminals along the supply chain, and the antecedents and consequences of the integration strategies. In this thesis integration strategy is termed Port Supply Chain Integration (PSCI) and the antecedents and the consequences of PSCI were identified and termed as Port Supply Chain Orientation (PSCO) and Port Performance (PP). A research model representing these causal relationships was developed through a comprehensive literature review and a semi-structured interview with practitioners and academics. The research used Structural Equation Modelling (SEM) to validate the constructs and rigorously test the relationships among them. Furthermore, a multi-group analysis was conducted in order to assess the applicability of the research model to different environments. The multi-group analysis tested whether the research model can be applied to two sample ports (A and B) and two sample groups (terminal operating companies and port users). The data used in these empirical analyses were collected from terminal operating companies, shipping companies and freight forwarders in South Korea, and the number of responses was 127. With regard to the results and findings of the empirical research, firstly, the three constructs, PSCO, PSCI and PP were successfully validated with the components identified from the literature review and interviews indicating that the three constructs are multi-dimensional concepts. Secondly, the empirical research showed that PSCO has a strong contribution to PSCI, and PSCI has a strong and positive impact on PP. Additionally PSCO, in turn, was found to influence positively and indirectly on PP through implementing PSCI. Finally the multi-group analysis showed that the research model can be equally applied to the two sample ports. However, the invariance test across two sample groups failed since a baseline model for the invariance test was not e