Wan-rong Jih

Dynamic vehicle routing using hybrid genetic algorithms

This paper presents a novel approach to solving the single-vehicle pickup and delivery problem with time windows and capacity constraints. While dynamic programming has been used to find the optimal routing to a given problem, it requires time exponential in the number of tasks. Therefore, it often fails to find the solutions under real-time conditions in an automated factory. This research explores anytime problem solving using genetic algorithms. By utilizing optimal but possibly partial solutions from dynamic programming, the hybrid genetic algorithms can produce near-optimal solutions for problems of sizes up to 25 percent bigger than what can be solved previously. This paper reports the experimental results of the proposed hybrid approach with four different crossover operators as well as three mutation operators. The experiments demonstrated the advantages of the hybrid approach with respect to dynamic task requests.

Applying power meters for appliance recognition on the electric panel

Recognition of appliances states is an import building block for making energy-efficiency schemes and providing energy-saving advice and performing automatic control. Several existing approachs use smart outlets or detectors to acquire the information of individual appliance and recognize the operating state. However, such approachs have to install numerous devices if they want to monitor the states of all appliances. This will increase the cost and complexity of installation and maintenance. Therefore, we develop an appliance recognition system which minimizing the scope of deployment. We install smart meters at single-point, distribution board, to measure the power consumption at circuit-level. In addition, to improve the recognition accuracy of our system and detect the state changes in real time, We use dynamic baysian network to take user behavior into account and Bayes filter to perform online inference. Finally, we design several experiments to compare our approach with some commonly used classifiers, such as Naive Bayes, k-Nearest Neighbor (kNN) and Support Vector Machine (SVM). Results shows that our model outperforms these classifiers and the accuracies of all appliances are greater than 92%. Furthermore, we also compare the results of Bayes filter with Viterbi algorithm, which is an offline inference method. The difference in accuracy of every appliance between Bayes filter and Viterbi algorithm is less than 1%.

Probabilistic models for concurrent chatting activity recognition

Recognition of chatting activities in social interactions is useful for constructing human social networks. However, the existence of multiple people involved in multiple dialogues presents special challenges. To model the conversational dynamics of concurrent chatting behaviors, this article advocates Factorial Conditional Random Fields (FCRFs) as a model to accommodate co-temporal relationships among multiple activity states. In addition, to avoid the use of inefficient Loopy Belief Propagation (LBP) algorithm, we propose using Iterative Classification Algorithm (ICA) as the inference method for FCRFs. We designed experiments to compare our FCRFs model with two dynamic probabilistic models, Parallel Condition Random Fields (PCRFs) and Hidden Markov Models (HMMs), in learning and decoding based on auditory data. The experimental results show that FCRFs outperform PCRFs and HMMs-like models. We also discover that FCRFs using the ICA inference approach not only improves the recognition accuracy but also takes significantly less time than the LBP inference method.

Context life cycle management in smart space environments

Life cycle management plays an essential role in context-aware systems, it needs to aware surrounding contexts and adapt to changing contexts in highly dynamic environments. Consequently, context management is different from traditional approaches due to the characteristics of contextual information are dynamic, transient, and fallible. Understanding the life cycle of a context is the key issue to maintain consistent contextual information. Accordingly, we propose an ontology-based model for representing, deducing, and managing consistent contextual information. In addition, an agent-based architecture supports the process of context life cycle management.

Parameter learning of personalized trust models in broker-based distributed trust management

Distributed trust management addresses the challenges of eliciting, evaluating and propagating trust for service providers on the distributed network. By delegating trust management to brokers, individual users can share their feedbacks for services without the overhead of maintaining their own ratings. This research proposes a two-tier trust hierarchy, in which a user relies on her broker to provide reputation rating about any service provider, while brokers leverage their connected partners in aggregating the reputation of unfamiliar service providers. Each broker collects feedbacks from its users on past transactions. To accommodate individual differences, personalized trust is modeled with a Bayesian network. Training strategies such as the expectation maximization (EM) algorithm can be deployed to estimate both server reputation and user bias. This paper presents the design and implementation of a distributed trust simulator, which supports experiments under different configurations. In addition, we have conducted experiments to show the following. 1) Personal rating error converges to below 5% consistently within 10,000 transactions regardless of the training strategy or bias distribution. 2) The choice of trust model has a significant impact on the performance of reputation prediction. 3) The two-tier trust framework scales well to distributed environments. In summary, parameter learning of trust models in the broker-based framework enables both aggregation of feedbacks and personalized reputation prediction.

Using family competition genetic algorithm in pickup and delivery problem with time window constraints

In this paper, we propose a novel research scheme to solve the single vehicle pickup and delivery problem (PDPTW) with time window constraints. The family competition genetic algorithm (FCGA) is a modern approach that has been successfully applied to solve the traveling salesman problem. We illustrate the FCGA and give the experimental results that show the FCGA is an effective algorithm for solving the single vehicle PDPTW. Genetic algorithms (GA) have been successful applied to solve the combinatorial computation problems. The family competition will improve the achievements for obtaining optimal solutions and the probability to hit the feasible solutions. By comparing FCGA with traditional GA, this excellent approach does not need enormous resources. Applying FCGA to single vehicle PDPTW, it succeeded in finding feasible solutions for all problems and obtained efficient results in our experimentation.

Energy-Aware agents for detecting nonessential appliances

In the past decades, the amount of electricity used by appliances has grown dramatically. As we are demanding more electricity, we should lower the damage to our environment by using energy efficiently. Conservation of energy by looking at ones habits and notifying them to turn off unnecessary appliances can help out a lot. This research develop a framework, which is able to recognize the operating state of every electrical appliance in a house and figure current user activity. By analyzing the behavior of using appliances, the correlation between activity and appliance can help to detect the nonessential appliance, which is the appliance does not participate in any user activity. The real user experimental results show 96.43% in recognizing the operating state of appliances and 72.66% in detecting nonessential appliances.

Agent-Based Context Consistency Management in Smart Space Environments

Context-aware systems in smart space environments must be aware of the context of their surroundings and adapt to changes in highly dynamic environments. Data management of contextual information is different from traditional approaches because the contextual information is dynamic, transient, and fallible in nature. Consequently, the capability to detect context inconsistency and maintain consistent contextual information are two key issues for context management. We propose an ontology-based model for representing, deducing, and managing consistent contextual information. In addition, we use ontology reasoning to detect and resolve context inconsistency problems, which will be described in a Smart Alarm Clock scenario.

Agent-Based Context-Aware Service in a Smart Space

Growing attention in recent decades has been devoted to implementation of methods of computational intelligence for seismic structural control synchronization of buildings and bridges, to reduce their responses to earthquakes. Seismic control synchronization is realized via programmable structural control at seismic excitations, with sensor technologies and synthesis of feedback control loads in regenerative force actuation network for protection of structures. The control synchronization with computational intelligence aims to return a structure with n-degree-of-freedom back to the equilibrium with dynamic switching commutation of actuator devices engaged in regenerative force actuation network. The network consists of a set of electromechanical devices positioned on different places into the structure. The synchronization is realized after activation when these devices absorb and dissipate a part of seismic energy. The actuator devices are connected with each other and their electronic help to share common electrical energy.

Utilizing descriptive documents for adaptive and reconfigurable M2M systems

Machine-to-Machine (M2M) has becomes an important research topic in the recent years. There are application choices for users, but these applications cannot exchange information with each other. Applications for different purposes cannot share the same devices, because the providers are different. Therefore, the users need an integrated framework that can easily configure devices and adapt to the user requirements. We propose three descriptive documents to facilitate the achievement of reconfiguration and adaptation. Results demonstrate our framework can seamlessly manage devices and promptly adapt to the environmental changes.