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The 2018 International Conference On Smart City and Intelligent Building ICSCIB 2018 |
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Organized by |
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Sponsored by |
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Organizer, Sponsors and Supporters |
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Organizer |
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Sponsors |
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Supporters |
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Conference Organizing Committee |
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General Chairs |
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Program Chairs |
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International Program Committee |
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Chairs of IPC |
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Members of IPC |
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Invited Speakers |
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Preface |
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Acknowledgements |
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About This Book |
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Contents |
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Building Energy Efficiency |
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Study on the Control Method of Temperature and Humidity Environment in Building Intelligent System |
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1 Introduction |
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2 Radial Basis Function (RBF) Neural Network and Orthogonal Least Squares Algorithm (OLS) |
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3 Weed Optimization Algorithm Based on RBF Neural Network Node Centers |
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4 Simulation and Analysis |
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4.1 Data Acquisition |
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4.2 Simulation Results |
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5 Conclusions |
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References |
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Research on Human Thermal Comfort Model Based on Multiple Physiological Parameters |
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1 Introduction |
34 |
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2 Methods |
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3 Experiments of Human Thermal Comfort |
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3.1 Experimental Environment |
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3.2 Design of Questionnaires |
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3.3 Test Conditions and Procedures |
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4 Establishment and Evaluation of the Human Thermal Comfort Model |
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4.1 Experimental Data |
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4.2 Establishment of the Human Thermal Comfort Model |
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4.3 Evaluation of the Human Thermal Comfort Model |
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5 Results and Discussion |
40 |
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6 Conclusions |
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References |
42 |
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Study on Thermal Comfort Model Based on Genetic Algorithm with Backpropagation Neural Network |
44 |
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1 Introduction |
44 |
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2 Establishment of Thermal Comfort Model |
45 |
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2.1 Physiological Signal Extraction |
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2.2 Feature Fusion of Physiological Signals |
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2.3 The Feature Optimization Based on Genetic Algorithm |
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2.4 The Principles and Construction of BP Neural Network |
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2.5 Identification of the Numbers of Neurons in Hidden Layers |
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3 Experiment and Results |
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3.1 Subjects and Facilities |
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3.2 Data Preprocessing |
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3.3 Establishment of BP Neural Network Model |
52 |
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3.4 Verification of Experimental Results |
52 |
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4 Conclusions and Discussion |
53 |
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References |
53 |
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An Optimization Model for PV and CCHP-Supplied Power System in Buildings |
55 |
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1 Introduction |
55 |
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2 Models of Individual Unit |
56 |
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2.1 Model of PV Unit |
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2.2 Model of ICE |
57 |
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2.3 Model of GF |
58 |
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2.4 Model of LiBr AC |
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3 Optimization Algorithm for System Dispatch |
59 |
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3.1 Optimizing Dispatch Modeling |
59 |
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3.2 Algorithm of Optimizing Dispatch |
60 |
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4 Algorithm Implementation and Results of the Case Study |
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4.1 Algorithm Implementation |
61 |
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4.2 Case Study |
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5 Conclusions |
64 |
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References |
64 |
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Control and Optimization of Indoor Environmental Quality Based on Model Prediction in Building |
65 |
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1 Introduction |
65 |
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2 The Model of Control System |
66 |
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2.1 Notations |
66 |
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2.2 The Indoor Environment Quality Control Mathematical Model |
67 |
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3 Analysis and Validation of Model |
71 |
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4 Model Predictive Control and Optimization |
72 |
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5 Analysis of Experiment and Result |
73 |
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6 Conclusions |
76 |
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References |
77 |
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Identifying Abnormal Energy Consumption Data of Lighting and Socket Based on Energy Consumption Characteristics |
78 |
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1 Introduction |
78 |
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2 The Classification of Data |
79 |
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3 Identification Method of Missing or Mutational Data |
81 |
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4 Identification Methods of Implicit Error Data |
82 |
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4.1 Threshold Method |
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4.2 Clustering Method |
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4.3 Identification Method Based on Energy Consumption Characteristics |
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5 Case Study |
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5.1 Classify the Energy-Usage Mode |
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5.2 Eliminate Abnormal Data by Clustering Method |
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5.3 Compare with Historic Energy Consumption Characteristic Lines |
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6 Conclusions |
91 |
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References |
91 |
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Construction Robot and Automation |
92 |
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An Improved Weight Control System for Slender Cigarette Production |
93 |
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1 Introduction |
94 |
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2 Structure of Control System |
94 |
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3 Weight Control Principle |
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3.1 Weight Signal Acquisition |
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3.2 Calculation of Cigarette Weight |
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3.3 Weight Control Algorithm |
97 |
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4 Experiment and Result Analysis |
99 |
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5 Conclusions |
99 |
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References |
100 |
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Design of Building Environment Mobile Monitoring and Safety Early Warning Robot |
102 |
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1 Introduction |
102 |
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2 Balance Principle Design of Two-Wheeled Robot |
103 |
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2.1 Mechanical Structure Design of Two-Wheeled Monitoring Robot |
103 |
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2.2 Two-Wheeled Robot Motion and Balance Analysis |
103 |
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2.3 Walking Upright Control Algorithm |
105 |
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3 System Architecture Design |
106 |
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4 System Software Program Design |
107 |
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4.1 Motion Control Program Design |
107 |
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4.2 Program Design of Environmental Monitoring Software |
109 |
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5 Conclusions |
109 |
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References |
110 |
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Application of Probabilistic Reasoning Algorithm in Indoor Positioning Based on WLAN |
111 |
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1 Introduction |
111 |
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2 Indoor Navigation System |
112 |
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3 Positioning Method Based on Fingerprint |
113 |
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4 Probabilistic Reasoning Algorithm |
114 |
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5 Experimental Results |
115 |
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6 Conclusions |
116 |
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References |
116 |
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Multiple Rotorcrafts Environment Map Fusion for Atmosphere Monitoring |
117 |
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1 Introduction |
118 |
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2 Model Establishment of Fluent’s Two-Dimensional Concentration Map |
119 |
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3 Mean Factor Algorithm |
121 |
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4 Simulation Experiment |
122 |
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5 Conclusions |
126 |
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References |
129 |
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Simulated Tests of Feedforward Active Noise Control (ANC) for Building Noise Cancellation |
130 |
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1 Introduction |
130 |
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2 Feedforward and Feedback ANC Systems |
131 |
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3 Simulation Studies |
134 |
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3.1 Narrowband Signal |
134 |
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3.2 The Mixed Noise |
136 |
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4 Conclusions |
136 |
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References |
138 |
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Research on Production Layout Design of Concrete Prefabricated Units Based on SLP |
139 |
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1 Introduction |
139 |
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2 Original Condition |
140 |
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3 Analysis of Logistics and Non-logistics |
142 |
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4 Draw the Unit Comprehensive Correlation Table |
143 |
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4.1 Comprehensive Correlation Calculation of Operation Units |
143 |
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4.2 Draw the Unit Comprehensive Correlation Table |
144 |
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5 The Drawing of Units Location of Comprehensive Operation |
145 |
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6 The Layout Design of the Manufacturing Area |
145 |
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7 Selection and Optimization of the Scheme |
146 |
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8 Conclusions |
146 |
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References |
148 |
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Intelligent Community and Urban Safety |
149 |
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Blockchain in Smart City Development—The Knowledge Governance Framework in Dynamic Alliance |
150 |
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1 Introduction |
150 |
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2 Background |
151 |
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2.1 On Smart City |
151 |
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2.2 The Blockchain |
152 |
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2.3 Dynamic Alliance (DA) |
155 |
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3 DA Knowledge Governance Based on Blockchain |
156 |
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3.1 Strategic Selection of Dynamic Alliance in Smart City for Cooperation |
156 |
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3.2 The Knowledge Governance in Dynamic Alliance Based on Blockchain |
160 |
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3.3 The Organization of DA of Smart City Based on Blockchain |
161 |
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4 Conclusions |
163 |
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References |
164 |
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Attendance and Security System Based on Building Video Surveillance |
166 |
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1 Introduction |
166 |
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2 Approach |
167 |
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2.1 Face Detection |
168 |
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2.2 Image Preprocessing |
168 |
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2.3 Face Recognition |
169 |
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2.4 Multi-frame Identification Based on Sliding Average |
170 |
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3 Experiment |
171 |
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3.1 Dataset |
171 |
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3.2 Platform |
171 |
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3.3 Experimental Results |
171 |
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3.4 Performance and Comparison |
172 |
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4 Discussion |
173 |
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5 Conclusions |
174 |
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References |
175 |
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Simulation Study on Collaborative Evacuation Among Stairs and Elevators in High-Rise Building |
176 |
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1 Introduction |
176 |
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2 Feasibility Analysis of Using Elevator as the Evacuation Tool |
177 |
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3 Simulation Experiment Setting |
179 |
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4 Results and Analysis |
180 |
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4.1 Partial Building Evacuation |
180 |
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4.2 Total Building Evacuation |
182 |
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5 Conclusions |
184 |
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References |
184 |
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Dynamic Emergency Evacuation System for Large Public Building |
186 |
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1 Introduction |
186 |
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2 Frame of Emergency Evacuation System |
187 |
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2.1 Frame of Information Input System |
187 |
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2.2 Frame of Main Control Module System |
188 |
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2.3 Frame of Implementation System |
189 |
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3 The Realization of the Emergency Evacuation System |
190 |
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3.1 The Realization of Information Input System |
190 |
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3.2 The Realization of Main Control Module System |
190 |
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3.3 The Realization of Implement System |
192 |
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3.4 Expectation |
192 |
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4 Case Study |
193 |
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5 Conclusion |
194 |
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References |
195 |
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Status of Intelligent Building Development of China—Questionnaire Analysis |
196 |
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1 Introduction |
197 |
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2 Questionnaire Construction |
198 |
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2.1 Section Ration |
198 |
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2.2 Structure of the Questionnaire |
198 |
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3 Results Analysis |
199 |
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3.1 General Information of the Interview |
199 |
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3.2 Application Status of the Building Intelligent |
200 |
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3.3 Application Status of the BAS (HVAC) |
201 |
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3.4 Cost |
203 |
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3.5 Client Requirement |
204 |
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3.6 Problems |
204 |
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4 Conclusions |
206 |
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References |
206 |
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Intelligentialization of Heating Ventilation Air Conditioning System |
208 |
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Regression Model of Wet-Bulb Temperature in an HVAC System |
209 |
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1 Introduction |
209 |
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2 Original Model of the Wet-Bulb Temperature |
211 |
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2.1 Model Formulation |
211 |
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2.2 Calculation Process |
211 |
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3 Regression Model of the Wet-Bulb Temperature |
212 |
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3.1 Generalized Model of Wet-Bulb Temperature |
212 |
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3.2 Regression Model of Wet-Bulb Temperature |
213 |
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4 Case Study |
214 |
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5 Conclusions |
217 |
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References |
217 |
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Research on Optimal Control Algorithm of Ice Thermal-Storage Air-Conditioning System |
218 |
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1 Introduction |
219 |
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2 Ice-Storage Air-Conditioning System Energy Consumption Modeling |
220 |
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2.1 Notations |
221 |
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2.2 Chiller Energy Consumption Model |
221 |
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2.3 Cooling Tower Energy Consumption Model |
222 |
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2.4 Pump Energy Consumption Model |
222 |
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3 Constraint-Based Nonlinear Multivariate Function Optimization Algorithm |
223 |
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4 Case Study |
224 |
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5 Experimental Results |
225 |
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6 Conclusions |
228 |
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References |
228 |
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Decentralized Optimization Algorithm for Parallel Pumps in HVAC Based on Log-Linear Model |
230 |
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1 Introduction |
231 |
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2 System Model and Problem Definition |
232 |
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2.1 Notations |
232 |
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2.2 Pumps Model and Its Decentralized Control System |
232 |
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3 Decentralized Optimization Algorithm Based on Log-Linear Model |
235 |
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3.1 Log-Linear Model |
235 |
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3.2 Decentralized Optimization Algorithm |
236 |
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4 Case Study |
237 |
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4.1 Experimental Setup |
237 |
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4.2 Results and Analysis |
238 |
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5 Conclusions |
240 |
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References |
241 |
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Partial Fault Detection of Cooling Tower in Building HVAC System |
242 |
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1 Introduction |
243 |
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2 Development Status |
244 |
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3 Model Description |
245 |
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4 Methodology |
246 |
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5 Simulation Studies |
248 |
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6 Conclusions |
250 |
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References |
251 |
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Application of Information Network and Control Network Integration Technology in Central Air Conditioning Data Management System |
252 |
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1 Introduction |
252 |
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2 Overall Architecture |
253 |
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3 System Overall Design |
254 |
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3.1 Performance-Based Design |
255 |
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3.2 Architecture Design |
255 |
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3.3 Modules Design |
256 |
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4 Software Implementation |
257 |
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4.1 Development Environment and Tools |
257 |
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4.2 Storage Optimization Strategy |
257 |
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4.3 The Implementation and User Interface |
258 |
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5 Conclusions |
258 |
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References |
259 |
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MFAC and Parameter Optimization for a Class of Models in HVAC |
260 |
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1 Introduction |
261 |
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2 The Model-Free Adaptive Control |
262 |
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3 Simulation Studies on Controlling Object with Uncertainty |
263 |
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3.1 Simulation Object and Parameter Settings |
263 |
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3.2 Analysis of Simulation Results |
266 |
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4 MFAC Parameter Optimization |
266 |
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4.1 The Basic Idea of the Simplex Method |
267 |
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4.2 Simplex Parameter Optimization Design Based on Simulink |
267 |
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4.3 Simulation on Optimizing the Controller Parameters of MFAC |
268 |
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5 Conclusions |
270 |
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References |
270 |
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A Controller Algorithm (ILC) for the Variable Differential Pressure Control of Freezing Water in a Central Air Conditioning System |
272 |
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1 Introduction |
272 |
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2 Variable Water Volume Air Conditioning Systems |
273 |
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3 Variable Differential Pressure Control |
274 |
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4 Variable Differential Pressure Control Based on Iterative Learning Algorithm |
275 |
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4.1 Selection of Ideal Trajectory of Differential Pressure |
275 |
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4.2 The Iterative Learning Control Unit |
276 |
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4.3 Simulation Experiments |
277 |
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5 Analysis of Energy Consumption |
279 |
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6 Conclusions |
280 |
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References |
280 |
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The Online Evaluation System of Chiller Plant in HVAC System |
281 |
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1 Introduction |
281 |
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2 Architecture of the Evaluation System |
282 |
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2.1 Software Architecture |
282 |
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2.2 Functional Architecture |
284 |
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3 Evaluation Contents and Methods |
284 |
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3.1 Notations |
284 |
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3.2 Evaluation Contents |
285 |
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3.3 Evaluation Methods |
286 |
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4 Evaluation Case |
287 |
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5 Conclusions |
288 |
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References |
289 |
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The Power Consumption Model of Chiller with Elman Neural Networks for On-line Prediction and Control |
290 |
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1 Introduction |
290 |
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2 Description of the Elman Neural Network |
291 |
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2.1 Notations |
291 |
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3 Model Input and Output Parameters |
292 |
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4 Description of Measurement System and Experimental Data |
292 |
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5 Development of the ENN Model |
293 |
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5.1 Evaluating Indicators of ENN Model |
293 |
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5.2 Data Preprocessing |
295 |
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5.3 The Network Architecture of ENN Model |
295 |
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5.4 ENN Model Training |
295 |
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5.5 ENN Model Testing |
296 |
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6 Application of the ENN Model |
297 |
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7 Conclusions |
297 |
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References |
298 |
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Information Technology and Intelligent Transportation Systems |
299 |
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Research on Driving Decisions in Winter and Summer Based on Survey Date |
300 |
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1 Introduction |
300 |
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2 The Survey Data |
301 |
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3 Based on Survey Date Analyze Vehicle Driving Characteristics |
302 |
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3.1 Speed Profile of Stopping Vehicles |
302 |
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3.2 Proportion of Stopping Vehicles |
303 |
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4 Analytical Model |
304 |
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5 Conclusions |
307 |
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References |
307 |
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The Performance Evaluation and Improvement of Urban Taxi Firms Using Data Envelopment Analysis and Benchmarking Approach |
308 |
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1 Introduction |
308 |
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2 Methodology |
309 |
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3 Data |
310 |
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4 Results |
311 |
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5 Conclusions |
314 |
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References |
314 |
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Research on the DV-Hop Location Algorithm Based on the Particle Swarm Optimization for the Automatic Driving Vehicle |
316 |
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1 Introduction |
316 |
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2 DV-Hop Algorithm Induction |
317 |
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3 NJPDH Algorithm Based on Adaptive Particle Swarm Optimization |
319 |
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3.1 Beacon Node Improvement |
319 |
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3.2 Beacon Node Improvements |
320 |
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4 Introduction of Particle Swarm Optimization Algorithm |
321 |
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4.1 Improvement of Inertia Weight |
322 |
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4.2 Active Factor Center Learning Particle Swarm Optimization Algorithm |
322 |
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4.3 Algorithm Procedure |
322 |
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5 Simulation Studies and Result Analysis |
323 |
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6 Conclusions |
325 |
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References |
326 |
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Multi-objective Optimization Coordination for Urban Arterial Roadway Based on Operational-Features |
327 |
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1 Introduction |
327 |
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2 Background Knowledge |
329 |
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2.1 Multi-objective Optimization Problem |
329 |
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2.2 Non-dominated Sorting Genetic Algorithm: NSGA-II |
329 |
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2.3 Selection of the Evaluation Indexes |
330 |
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3 Modeling the Multi-objective Optimization Problem on the Urban Arterial Roadway |
331 |
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3.1 Some Main Parameters for Describing the Arterial Traffic Coordinated Control |
332 |
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3.2 Webster’s Arterial Traffic Coordinated Control Model |
332 |
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3.3 An Improved MAXBAND Arterial Traffic Coordinated Control Model |
333 |
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3.4 The Operational-Feature-Based Urban Arterial Traffic Coordinated Control Model |
334 |
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4 Numerical Experiment |
335 |
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5 Conclusions |
337 |
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References |
338 |
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Applicability Analytic of Closed Intersection Along Tramway Based on Simulation |
339 |
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1 Introduction |
339 |
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2 The Applicability of Closed Intersection |
340 |
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2.1 Traffic Organization After Closing Intersection |
340 |
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2.2 Intersection Surrounding Environment Reference Conditions |
342 |
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3 Simulation Modeling |
342 |
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4 Experiment and Result Analysis |
344 |
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4.1 Data Survey and Experimental Design |
344 |
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4.2 Result Analysis of Bypass Flow Ratio |
346 |
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4.3 Result Analysis of Left-Turn Capacity and Traffic Flow |
346 |
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5 Conclusions |
347 |
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References |
347 |
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Vehicle Scheduling Optimization of Urban Distribution Considering Traffic Control |
349 |
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1 Introduction |
349 |
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2 Problem Description and Modeling Conditions |
351 |
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3 Scheduling Optimization Model and Algorithm of Urban Distribution Considering Traffic Control |
351 |
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3.1 Variables and Parameters |
351 |
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3.2 Model Construction |
353 |
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3.3 A Hybrid Genetic Algorithm Considering Urban Transport Control Constraints |
354 |
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4 Numerical Experiments and Result Analysis |
356 |
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5 Conclusions and Outlook |
358 |
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References |
358 |
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The Warrant of Slip Lane at Single-Lane Roundabout |
359 |
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1 Introduction |
359 |
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2 Slip Lane Setting Type and Traffic Flow Characteristics |
360 |
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2.1 Type of Slip Lane at Roundabout |
360 |
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2.2 Traffic Flow Characteristics of Slip Lane |
361 |
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3 Benefit Analysis |
363 |
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3.1 Simulation Conditions |
363 |
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3.2 Results |
364 |
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3.3 Critical Condition Analysis |
365 |
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3.4 Other Influencing Factors |
366 |
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4 Conclusions |
366 |
|
|
References |
367 |
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A New Method for the Minimum Concave Cost Transportation Problem in Smart Transportation |
368 |
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1 Introduction |
368 |
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2 Deterministic Annealing and Neural Networks |
369 |
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3 Numerical Results |
372 |
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4 Conclusions |
373 |
|
|
References |
374 |
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Study on the Characteristics of Vehicle Lane-Changing in the Intersection |
375 |
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1 Introduction |
375 |
|
|
2 Drivers’ Lane-Changing Behavior Analysis |
376 |
|
|
3 Establishing the Vehicle Lane-Changing Model in the Intersection |
379 |
|
|
4 Analyze the Behavior of Overtaking Lane-Changing in the Intersection |
381 |
|
|
5 Conclusions |
384 |
|
|
References |
384 |
|
|
New Generation Intelligent Building Platform Techniques |
386 |
|
|
A P2P Algorithm for Energy Saving of a Parallel-Connected Pumps System |
387 |
|
|
1 Introduction |
387 |
|
|
2 Problem Description |
389 |
|
|
2.1 Notations |
389 |
|
|
2.2 Mathematical Model |
390 |
|
|
3 Distributed Algorithm |
392 |
|
|
4 Experimental Results |
393 |
|
|
5 Conclusions |
396 |
|
|
References |
396 |
|
|
A Distributed Algorithm for Building Space Topology Matching |
398 |
|
|
1 Introduction |
398 |
|
|
2 Problem Description |
399 |
|
|
2.1 Notations |
400 |
|
|
2.2 Problem Assumptions |
400 |
|
|
2.3 Mathematical Model |
401 |
|
|
2.4 Objective |
402 |
|
|
3 Building Space Topology Matching Algorithm |
402 |
|
|
4 Building Space Topology Matching Algorithm |
404 |
|
|
5 Experiments |
405 |
|
|
6 Conclusions |
407 |
|
|
References |
407 |
|
|
Decentralized Differential Evolutionary Algorithm for Large-Scale Networked Systems |
408 |
|
|
1 Introduction |
408 |
|
|
2 Problem Formulation |
409 |
|
|
2.1 Centralized Optimization Problem |
410 |
|
|
2.2 Neighborhood of Subsystems |
410 |
|
|
2.3 Decentralized Optimization Problem |
410 |
|
|
2.4 Notations |
411 |
|
|
3 Decentralized Differential Evolutionary Algorithm |
411 |
|
|
4 Experimental Setup |
414 |
|
|
5 Results and Discussions |
414 |
|
|
6 Conclusions |
415 |
|
|
Appendix |
417 |
|
|
References |
418 |
|
|
Intelligent Building Fault Diagnosis Based on Wavelet Transform and Bayesian Network |
419 |
|
|
1 Introduction |
419 |
|
|
2 Bayesian Reasoning |
420 |
|
|
3 Supply and Distribution Network Fault Feature Extraction Method |
421 |
|
|
3.1 Electrical Characteristics Extraction Method |
421 |
|
|
3.2 Component Switch Signal Feature Extraction Method |
424 |
|
|
3.3 Comprehensive Feature Extraction Method |
424 |
|
|
4 Fault Diagnosis Process for Power Distribution Based on Bayesian |
425 |
|
|
5 Simulation Studies |
428 |
|
|
5.1 Supply Network Fault Model and Simulation |
428 |
|
|
5.2 Simulation Results and Analysis |
429 |
|
|
6 Summary |
432 |
|
|
References |
432 |
|
|
Fault Location of Distribution Network for Wavelet Packet Energy Moment of Dragonfly Algorithm |
433 |
|
|
1 Introduction |
433 |
|
|
2 Wavelet Neural Network |
434 |
|
|
2.1 Structure of Wavelet Neural Network |
434 |
|
|
2.2 Training of Wavelet Neural Network |
435 |
|
|
3 Power Distribution Network Fault Feature Extraction |
436 |
|
|
3.1 Wavelet Packet Energy Moment Principle |
436 |
|
|
3.2 Fault Feature Extraction Verification |
436 |
|
|
4 Fault Locating of Wavelet Neural Network Based on DA |
437 |
|
|
4.1 Dragonfly Algorithm |
437 |
|
|
4.2 Wavelet Neural Network Fault Location Process Based on Dragonfly Algorithm |
440 |
|
|
5 Simulation Studies |
441 |
|
|
5.1 Model and Simulation |
441 |
|
|
5.2 Results and Analysis |
441 |
|
|
6 Conclusions |
445 |
|
|
References |
446 |
|
|
Graphical Programming Language Design for Decentralized Building Intelligent System |
447 |
|
|
1 Introduction |
447 |
|
|
2 Requirements of Decentralized System Graphical Programming Language |
448 |
|
|
3 Graphical Programming Language Design |
449 |
|
|
3.1 Graphic Elements Design |
449 |
|
|
3.2 Interface of Graphic Elements |
452 |
|
|
4 Graphical Programming Case |
452 |
|
|
5 Conclusions and Outlook |
454 |
|
|
References |
455 |
|
|
Insect Intelligent Building (I2B): A New Architecture of Building Control Systems Based on Internet of Things (IoT) |
456 |
|
|
1 Introduction |
456 |
|
|
2 The New Architecture and Key Functional Features |
458 |
|
|
3 Theoretical Foundations |
461 |
|
|
4 Case Studies |
462 |
|
|
5 Conclusions |
464 |
|
|
References |
465 |
|
|
Smart Home and Smart Utility |
466 |
|
|
The Fault Diagnosis Model Established Based on RVM |
467 |
|
|
1 Introduction |
467 |
|
|
2 The Model of Relevance Vector Machine |
468 |
|
|
2.1 Selection of Kernel Functions |
470 |
|
|
2.2 Multimode Classification of RVM |
470 |
|
|
3 Case Verification: Establishment of Building Electrical Fault Diagnosis Model Based on RVM |
471 |
|
|
3.1 Establishment of RVM Fault Diagnosis Model |
472 |
|
|
3.2 The Choice of RVM Kernel Function |
472 |
|
|
3.3 Simulation Experiment of Electrical Fault Diagnosis Based on RVM |
473 |
|
|
4 Conclusions |
474 |
|
|
References |
474 |
|
|
PLC-Based Intelligent Home Control System |
475 |
|
|
1 Introduction |
475 |
|
|
2 Overall Design of the System |
476 |
|
|
3 PID Control Algorithm Based on BP Neural Network |
477 |
|
|
3.1 Algorithm Principle |
477 |
|
|
3.2 Simulation Studies |
480 |
|
|
4 Conclusions |
481 |
|
|
A Classification-Based Occupant Detection Method for Smart Home Using Multiple-WiFi Sniffers |
483 |
|
|
1 Introduction |
483 |
|
|
2 Basic Assumptions and the System Framework |
484 |
|
|
3 Multiple WiFi Sniffers-Based Classification Model for Occupant Counting |
486 |
|
|
3.1 Accuracy Analysis for Multiple-WiFi Sniffers-Based Binary Location Classifier |
487 |
|
|
3.2 Neural Networks Based Binary Classifier Design |
488 |
|
|
3.3 Occupant Counting |
489 |
|
|
4 Experimental Results |
489 |
|
|
4.1 Experimental Setup |
489 |
|
|
4.2 Performance Evaluations and Discussion |
490 |
|
|
5 Conclusions and Future Work |
492 |
|
|
References |
492 |
|
|
A p-Persistent Frequent Itemsets with 1-RHS Based Correction Algorithm for Improving the Performance of WiFi-Based Occupant Detection Method |
494 |
|
|
1 Introduction |
494 |
|
|
2 Basic Assumptions and System Model |
495 |
|
|
3 Design of k-Extension of High Frequent Itemsets Based Correction Algorithm for Occupant Detection |
497 |
|
|
3.1 Discussion on the Characteristics of the k-Extension of High-Frequent Itemsets |
497 |
|
|
3.2 Design for p-Persistent Frequent Itemsets with 1-Right-Hand-Side (RHS)-Based Correction Algorithm for Occupant Detection |
498 |
|
|
4 Experimental Results |
499 |
|
|
5 Conclusions and Future Work |
500 |
|
|
References |
501 |
|
|
Day-Ahead Short-Term Optimization of Renewable Energy of Microgrid in Multiple Timescales |
502 |
|
|
1 Introduction |
502 |
|
|
2 Optimization Principle |
503 |
|
|
3 Microgrid Optimal Operation in Short Time |
504 |
|
|
4 Case Study |
505 |
|
|
5 Results and Discussion |
507 |
|
|
6 Conclusions |
510 |
|
|
References |
510 |
|
|
Modeling of Multiple Heating Substations Based on Long Short-Term Memory Networks |
511 |
|
|
1 Introduction |
511 |
|
|
2 The Principle of LSTM Network |
512 |
|
|
3 Modeling of Multiple Heating Substations Based on LSTM |
512 |
|
|
3.1 Multiple Heating Substations Model Structures |
512 |
|
|
3.2 Data Selection and Preprocessing |
514 |
|
|
3.3 Model Structure and Parameters Based on LSTM |
514 |
|
|
4 Test Results |
516 |
|
|
5 Conclusions |
519 |
|
|
References |
520 |
|
|
The Elman Network of Heat Load Forecast Based on the Temperature and Sunlight Factor |
521 |
|
|
1 Introduction |
521 |
|
|
2 Heat Load Forecasting Based on Elman Neural Network |
522 |
|
|
2.1 Structure of Elman Neural Network |
522 |
|
|
2.2 Learning Process of Elman Neural Network |
523 |
|
|
3 Model Establishment |
523 |
|
|
3.1 Selection of Experimental Data |
524 |
|
|
3.2 Setting of Sunshine Factors |
525 |
|
|
4 Algorithm Implementation |
525 |
|
|
5 Analysis of Forecast Results |
526 |
|
|
5.1 Comparison of Prediction Results of Multiple Neural Networks |
526 |
|
|
5.2 Forecast Result of Adding Sunshine Factor into Elman Network |
526 |
|
|
6 Conclusions |
530 |
|
|
References |
531 |
|
|
Theoretical Study on Even Heating of Single Pipe Heating System |
533 |
|
|
1 Introduction |
534 |
|
|
2 Heating System Model |
534 |
|
|
2.1 Physical Model |
534 |
|
|
2.2 Mathematical Model |
536 |
|
|
3 Theoretical Analysis and Result Discussion |
537 |
|
|
4 Conclusions |
540 |
|
|
References |
541 |
|
|
Illumination Variation Similarity Based Fault Diagnosis for HV-LED Lamp Driven by Segmented Linear Driver |
542 |
|
|
1 Introduction |
543 |
|
|
2 Segmented Linear Solution for HV-LED Lamp |
543 |
|
|
3 Proposed Methods |
545 |
|
|
4 Experimental Results |
546 |
|
|
5 Conclusions |
547 |
|
|
Appendix |
548 |
|
|
References |
550 |
|
|
Point Illumination Calculation Method in Special-Shaped Space |
551 |
|
|
1 Introduction |
551 |
|
|
2 Problem Description |
552 |
|
|
2.1 Calculation of Direct Illumination |
552 |
|
|
2.2 Reflectivity Illuminance Calculation |
553 |
|
|
2.3 Spatial Model Establishment |
555 |
|
|
3 Parameter Assignment Analysis |
556 |
|
|
3.1 Reflection Calculations |
557 |
|
|
3.2 Calculation Step |
558 |
|
|
4 Analysis of Simulation Results |
560 |
|
|
5 Concluding Remarks |
561 |
|
|
References |
563 |
|
|
Smart Underground Space |
564 |
|
|
Device-Free Activity Recognition for Underground Spaces Based on Convolutional Neural Network |
565 |
|
|
1 Introduction |
565 |
|
|
2 Preliminaries |
566 |
|
|
3 The Under-Sense System |
567 |
|
|
3.1 System Overview |
567 |
|
|
3.2 Signal Preprocessing and Image Construction |
567 |
|
|
3.3 CNN-Based Feature Extraction and Classification |
569 |
|
|
4 Experimental Results |
570 |
|
|
4.1 Experiment Setup |
571 |
|
|
4.2 Overall Performance |
571 |
|
|
5 Conclusions |
572 |
|
|
References |
572 |
|
|
A Decentralized Parallel Kalman Filter in Multi-sensor System for Data Verification |
574 |
|
|
1 Introduction |
574 |
|
|
2 Problem Formulation |
576 |
|
|
3 Unconstrained and Decentralized Constrained Kalman Filter |
577 |
|
|
3.1 Unconstrained Kalman Filter |
577 |
|
|
3.2 Decentralized Constrained Kalman Filter |
577 |
|
|
4 Simulation Results |
580 |
|
|
5 Conclusions |
582 |
|
|
References |
582 |
|
|
DXF File Topological Information Extraction and Storage for Decentralized Distribution Network |
584 |
|
|
1 Introduction |
584 |
|
|
2 Extraction of AutoCAD Map Metadata Repository |
585 |
|
|
3 Topological Information Configuration and Adjacent Node Identification Rules |
585 |
|
|
3.1 CPN Topological Information Configuration |
585 |
|
|
3.2 Identification Rules of Adjacent Nodes |
586 |
|
|
4 Storage Structure of Global Topology |
587 |
|
|
5 Extracting and Storing the Topological Information of Typical Distribution Network |
588 |
|
|
5.1 The Project Design of Typical Distribution Network |
588 |
|
|
5.2 Result Analysis |
590 |
|
|
6 Conclusions |
591 |
|
|
References |
591 |
|
|
Research on Underground Device Operation and Maintenance Management System Based on BIMserver |
592 |
|
|
1 Introduction |
592 |
|
|
2 Overview of O&M Management Based on BIM |
593 |
|
|
2.1 BIM-Based Visual O&M Management |
593 |
|
|
2.2 O&M Information Management |
594 |
|
|
3 O&M Platform Development Technology |
594 |
|
|
3.1 O&M System Construction Principle |
594 |
|
|
3.2 The Display Technology of 3D Model |
595 |
|
|
4 IFC-Based Entity Expression and Analysis |
596 |
|
|
4.1 The Analysis of IFC Files |
596 |
|
|
4.2 O&M System Data Transfer |
598 |
|
|
5 Conclusions |
599 |
|
|
References |
599 |
|
|
A Fully Distributed Genetic Algorithm for Global Optimization of HVAC Systems |
600 |
|
|
1 Introduction |
600 |
|
|
2 System Model and Problem Definition |
602 |
|
|
2.1 Cost Function |
603 |
|
|
2.2 Constraints Establishment |
605 |
|
|
3 Decentralized Optimization Algorithm Based on Log-Linear Model |
605 |
|
|
3.1 Algorithm Design |
606 |
|
|
4 Application to HVAC Systems |
607 |
|
|
4.1 Experimental Setup |
607 |
|
|
4.2 Results and Analysis |
608 |
|
|
5 Conclusions |
610 |
|
|
References |
610 |
|
|
Open-Neutral Fault Detection in Underground Space Based on Genetic Support Vector Machine |
612 |
|
|
1 Introduction |
612 |
|
|
1.1 Harmonic Characteristics |
613 |
|
|
1.2 Neutral Voltage Offset Analysis |
614 |
|
|
2 Open-Neutral Fault Detection Based on Genetic Support Vector Machine |
615 |
|
|
2.1 Kernel Function Selection |
616 |
|
|
2.2 SVM Parameters Selection Based on Genetic Algorithm Optimization |
617 |
|
|
3 Simulation Studies |
618 |
|
|
4 Conclusions |
620 |
|
|
References |
621 |
|
|
Author Index |
622 |
|