【学术会议前沿信息|科研必备】IEEE/ACM双出版-EI快速检索！先进算法与控制工程、电气技术与自动化工程、AI智能赋能数字创意设计、虚拟现实与交互设计四大领域2026春之约！

【学术会议前沿信息|科研必备】IEEE/ACM双出版-EI快速检索！先进算法与控制工程、电气技术与自动化工程、AI智能赋能数字创意设计、虚拟现实与交互设计四大领域2026春之约！

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前言

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🧮 第九届先进算法与控制工程国际学术会议（ICAACE 2026）

2026 9th International Conference on Advanced Algorithms and Control Engineering

• ⏰ 会议时间：2026年3月20-22日
• 📍 会议地点：中国·济南
• ✨ 会议亮点：同济大学与山东师范大学联合主办，IEEE出版保证，一周左右快速录用，在泉城探讨算法与控制工程的前沿融合。
• 🔍 收录检索：IEEE Xplore, EI Compendex, Scopus
• 👥 适合人群：专注于智能算法设计、控制理论、系统优化等方向的硕士、博士及青年教师。
• 领域：智能控制算法、系统优化

import numpy as np
from scipy.linalg import solve_continuous_are

class AdaptiveModelPredictiveControl:
    """自适应模型预测控制(MPC)算法"""
    
    def __init__(self, system_order=3, horizon=10):
        self.n = system_order
        self.N = horizon
        self.Q = np.eye(self.n)  # 状态权重
        self.R = np.eye(1)       # 控制权重
        
    def solve_mpc(self, current_state, reference_trajectory):
        """实时MPC求解器"""
        # 构建优化问题
        n_vars = self.N * (self.n + 1)  # 状态 + 控制
        
        # 简化的二次规划形式
        H = self.build_hessian_matrix()
        f = self.build_gradient_vector(current_state, reference_trajectory)
        
        # 约束条件：状态和控制约束
        A_eq, b_eq = self.build_equality_constraints(current_state)
        
        # 求解QP问题（简化解法）
        # 这里使用拉格朗日乘子法简化求解
        K = self.calculate_lqr_gain()  # LQR作为MPC的简化
        u_optimal = -K @ current_state
        
        return u_optimal
    
    def calculate_lqr_gain(self):
        """计算LQR反馈增益（MPC的简化）"""
        # 系统动力学（随机生成示例）
        A = np.random.randn(self.n, self.n) * 0.9
        B = np.random.randn(self.n, 1)
        
        # 求解代数Riccati方程
        P = solve_continuous_are(A, B, self.Q, self.R)
        
        # 计算最优反馈增益
        K = np.linalg.inv(self.R) @ B.T @ P
        return K
    
    def adaptive_tuning(self, tracking_error):
        """基于跟踪误差的自适应参数调整"""
        # 自适应调整权重矩阵
        error_norm = np.linalg.norm(tracking_error)
        
        # 根据误差调整控制强度
        if error_norm > 1.0:
            self.R *= 0.9  # 加强控制
        elif error_norm < 0.1:
            self.R *= 1.1  # 减弱控制
        
        return self.R

class SwarmIntelligenceOptimizer:
    """群体智能优化算法"""
    
    def particle_swarm_optimization(self, objective_func, dim=5, n_particles=30):
        """粒子群优化算法"""
        # 初始化粒子
        particles = np.random.randn(n_particles, dim)
        velocities = np.random.randn(n_particles, dim) * 0.1
        
        # 个体最优和全局最优
        pbest = particles.copy()
        pbest_values = np.array([objective_func(p) for p in particles])
        gbest = particles[np.argmin(pbest_values)]
        gbest_value = np.min(pbest_values)
        
        # 优化参数
        w = 0.729  # 惯性权重
        c1 = 1.494  # 个体学习因子
        c2 = 1.494  # 社会学习因子
        
        for iteration in range(100):
            for i in range(n_particles):
                # 更新速度
                r1, r2 = np.random.rand(2)
                velocities[i] = (w * velocities[i] +
                                c1 * r1 * (pbest[i] - particles[i]) +
                                c2 * r2 * (gbest - particles[i]))
                
                # 更新位置
                particles[i] += velocities[i]
                
                # 评估适应度
                current_value = objective_func(particles[i])
                
                # 更新个体最优
                if current_value < pbest_values[i]:
                    pbest[i] = particles[i]
                    pbest_values[i] = current_value
                    
                    # 更新全局最优
                    if current_value < gbest_value:
                        gbest = particles[i]
                        gbest_value = current_value
        
        return gbest, gbest_value

⚡ 第三届电气技术与自动化工程国际学术会议（ETAE 2026）

The 3rd International Conference on Electrical Technology and Automation Engineering

• ⏰ 会议时间：2026年3月20-22日
• 📍 会议地点：中国·深圳
• ✨ 会议亮点：IEEE出版护航，3-10个工作日内回复，在创新之都深圳聚焦电气自动化技术的新成果与新应用。
• 🔍 收录检索：EI Compendex, Scopus, IEEE Xplore
• 👥 适合人群：从事电气工程、工业自动化、智能控制等领域的科研人员、工程师及研究生。
• 领域：智能电网、电力电子控制

import numpy as np
from scipy import signal

class PowerQualityEnhancer:
    """电能质量增强与谐波抑制算法"""
    
    def __init__(self, sampling_freq=10000, fundamental_freq=50):
        self.fs = sampling_freq
        self.f0 = fundamental_freq
        
    def adaptive_harmonic_filter(self, voltage_signal, harmonic_orders=[3, 5, 7]):
        """自适应谐波滤波器"""
        t = np.arange(len(voltage_signal)) / self.fs
        filtered_signal = voltage_signal.copy()
        
        for order in harmonic_orders:
            harmonic_freq = self.f0 * order
            
            # 生成参考信号
            ref_sin = np.sin(2 * np.pi * harmonic_freq * t)
            ref_cos = np.cos(2 * np.pi * harmonic_freq * t)
            
            # LMS自适应滤波
            w_sin, w_cos = 0.0, 0.0
            mu = 0.01  # 学习率
            
            for i in range(len(voltage_signal)):
                # 估计谐波分量
                harmonic_est = w_sin * ref_sin[i] + w_cos * ref_cos[i]
                
                # 误差信号
                error = voltage_signal[i] - harmonic_est
                
                # 更新权重
                w_sin += 2 * mu * error * ref_sin[i]
                w_cos += 2 * mu * error * ref_cos[i]
                
                # 从原始信号中减去谐波
                filtered_signal[i] -= harmonic_est
        
        return filtered_signal
    
    def reactive_power_compensation(self, voltage, current):
        """动态无功功率补偿算法"""
        # 计算瞬时有功和无功功率
        S = voltage * np.conj(current)  # 复功率
        
        # 分离有功和无功分量
        P = np.real(S)  # 有功功率
        Q = np.imag(S)  # 无功功率
        
        # 目标：将功率因数提高到0.95以上
        target_pf = 0.95
        target_Q = P * np.tan(np.arccos(target_pf))
        
        # 计算需要补偿的无功功率
        Q_compensation = Q - target_Q
        
        # 生成补偿信号（简化）
        compensation_signal = np.sign(Q_compensation) * np.sqrt(np.abs(Q_compensation))
        
        return compensation_signal

class MicrogridEnergyManager:
    """微电网能量管理与优化调度"""
    
    def optimal_power_dispatch(self, load_demand, renewable_generation, battery_soc):
        """最优功率调度算法"""
        n_time_slots = len(load_demand)
        
        # 决策变量：电网购电、电池充放电、柴油发电
        grid_power = np.zeros(n_time_slots)
        battery_power = np.zeros(n_time_slots)
        diesel_power = np.zeros(n_time_slots)
        
        # 成本参数
        grid_price = np.array([0.6 if 8 <= i < 22 else 0.3 for i in range(n_time_slots)])  # 峰谷电价
        diesel_cost = 0.8  # 元/kWh
        battery_degradation_cost = 0.1  # 元/kWh
        
        for t in range(n_time_slots):
            net_load = load_demand[t] - renewable_generation[t]
            
            # 优化决策（简化启发式规则）
            if net_load > 0:  # 需要供电
                # 优先使用电池（如果SOC允许）
                if battery_soc > 0.3 and battery_soc > net_load * 0.1:
                    battery_discharge = min(net_load, battery_soc * 10)  # 假设电池容量系数
                    battery_power[t] = -battery_discharge
                    net_load -= battery_discharge
                
                # 然后使用电网（考虑电价）
                if grid_price[t] < diesel_cost:
                    grid_power[t] = min(net_load, 50)  # 电网功率限制
                    net_load -= grid_power[t]
                
                # 最后使用柴油发电机
                if net_load > 0:
                    diesel_power[t] = net_load
            else:  # 过剩发电
                excess_power = -net_load
                # 优先给电池充电
                if battery_soc < 0.9:
                    battery_charge = min(excess_power, (0.9 - battery_soc) * 10)
                    battery_power[t] = battery_charge
                    excess_power -= battery_charge
                
                # 多余的可卖回电网
                if excess_power > 0:
                    grid_power[t] = -excess_power  # 负值表示向电网送电
        
        return grid_power, battery_power, diesel_power

🎨 第二届人工智能赋能数字创意设计国际学术会议（AIEDCD 2026）

The 2nd International Conference on AI-Enabled Digital Creative Design

• ⏰ 会议时间：2026年3月27-29日
• 📍 会议地点：中国·北京 & 意大利（线上线下结合）
• ✨ 会议亮点：双会场国际交流，三日极速审稿，探索人工智能与艺术设计的跨界融合与创新应用。
• 🔍 收录检索：Scopus, Springer Nature Link, Google Scholar
• 👥 适合人群：致力于AIGC、数字艺术、创意计算、智能设计等交叉学科的研究者与设计师。
• 领域：AIGC、创意生成、风格迁移

import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F

class NeuralStyleTransfer:
    """基于神经网络的风格迁移算法"""
    
    def __init__(self, content_weight=1e4, style_weight=1e-2):
        self.content_weight = content_weight
        self.style_weight = style_weight
        
    def compute_content_loss(self, content_features, generated_features):
        """计算内容损失"""
        return F.mse_loss(generated_features, content_features)
    
    def gram_matrix(self, features):
        """计算Gram矩阵（风格表示）"""
        b, c, h, w = features.size()
        features_reshaped = features.view(b * c, h * w)
        gram = torch.mm(features_reshaped, features_reshaped.t())
        return gram.div(b * c * h * w)
    
    def compute_style_loss(self, style_features_list, generated_features_list):
        """计算风格损失"""
        style_loss = 0
        for style_feat, gen_feat in zip(style_features_list, generated_features_list):
            style_gram = self.gram_matrix(style_feat)
            gen_gram = self.gram_matrix(gen_feat)
            style_loss += F.mse_loss(gen_gram, style_gram)
        return style_loss
    
    def total_variation_loss(self, image):
        """总变分损失（平滑性约束）"""
        h_diff = image[:, :, 1:, :] - image[:, :, :-1, :]
        w_diff = image[:, :, :, 1:] - image[:, :, :, :-1]
        return (h_diff.abs().mean() + w_diff.abs().mean())

class CreativeGAN:
    """创意生成对抗网络"""
    
    def __init__(self, latent_dim=100, style_dim=50):
        self.latent_dim = latent_dim
        self.style_dim = style_dim
        
    def generate_creative_design(self, text_prompt, style_vector):
        """基于文本提示和风格向量的创意生成"""
        # 文本编码
        text_embedding = self.encode_text(text_prompt)
        
        # 风格融合
        combined_latent = self.fuse_latent_vectors(text_embedding, style_vector)
        
        # 生成图像
        generated_image = self.generator(combined_latent)
        
        return generated_image
    
    def encode_text(self, text_prompt):
        """文本编码器（简化）"""
        # 使用预训练文本模型获取嵌入
        # 这里用随机向量模拟
        return np.random.randn(self.latent_dim)
    
    def fuse_latent_vectors(self, text_vector, style_vector):
        """潜在向量融合"""
        # 注意力融合机制
        attention_weights = self.compute_cross_attention(text_vector, style_vector)
        fused_vector = attention_weights * text_vector + (1 - attention_weights) * style_vector
        return fused_vector

class AICompositionAssistant:
    """AI构图与设计辅助算法"""
    
    def rule_of_thirds(self, image, objects):
        """三分法则构图优化"""
        height, width = image.shape[:2]
        third_x = width / 3
        third_y = height / 3
        
        # 计算兴趣点的位置
        interest_points = []
        for obj in objects:
            cx, cy = obj['center']
            # 计算到三分点的距离
            distances = []
            for i in range(1, 4):
                for j in range(1, 4):
                    grid_x = i * third_x
                    grid_y = j * third_y
                    dist = np.sqrt((cx - grid_x)**2 + (cy - grid_y)**2)
                    distances.append(dist)
            
            # 如果当前位置不好，建议移动方向
            if min(distances) > min(third_x, third_y) * 0.5:
                # 找到最近的三分点
                best_idx = np.argmin(distances)
                best_x = ((best_idx % 3) + 1) * third_x
                best_y = ((best_idx // 3) + 1) * third_y
                
                interest_points.append({
                    'current': (cx, cy),
                    'suggested': (best_x, best_y),
                    'movement_vector': (best_x - cx, best_y - cy)
                })
        
        return interest_points
    
    def color_harmony_analysis(self, color_palette):
        """色彩和谐度分析"""
        # 将RGB转换到HSV空间
        hsv_colors = self.rgb_to_hsv(color_palette)
        
        # 计算色相分布
        hues = hsv_colors[:, 0]
        
        # 分析色相关系
        harmony_score = 0
        n_colors = len(hues)
        
        for i in range(n_colors):
            for j in range(i+1, n_colors):
                hue_diff = abs(hues[i] - hues[j])
                
                # 检查是否属于和谐色相关系
                if hue_diff < 30:  # 类似色
                    harmony_score += 1
                elif 60 < hue_diff < 120:  # 对比色
                    harmony_score += 0.8
                elif 150 < hue_diff < 210:  # 互补色
                    harmony_score += 0.6
        
        # 归一化分数
        max_possible = n_colors * (n_colors - 1) / 2
        harmony_score = harmony_score / max_possible if max_possible > 0 else 0
        
        return harmony_score

🥽 第二届人工智能、虚拟现实与交互设计国际学术会议（AIVRID 2026）

The 2nd International Conference on Artificial Intelligence, Virtual Reality and Interaction Design

• ⏰ 会议时间：2026年3月27-29日
• 📍 会议地点：广东省东莞市
• ✨ 会议亮点：ACM出版社出版，EI/Scopus双检索稳定快速，在世界工厂探讨智能交互与虚拟现实的技术突破。
• 🔍 收录检索：EI Compendex, Scopus, ACM Digital Library
• 👥 适合人群：从事人机交互、虚拟现实技术、智能用户体验等前沿方向的研究人员与开发者。
• 领域：VR交互、眼动追踪、沉浸式体验

import numpy as np
from collections import deque

class GazePredictionModel:
    """基于深度学习的视线预测模型"""
    
    def __init__(self, sequence_length=10):
        self.seq_len = sequence_length
        self.gaze_history = deque(maxlen=sequence_length)
        
    def predict_next_gaze(self, current_gaze, head_pose, scene_saliency):
        """预测下一时刻的视线位置"""
        # 特征提取
        gaze_features = self.extract_gaze_features(current_gaze)
        head_features = self.extract_head_features(head_pose)
        saliency_features = self.extract_saliency_features(scene_saliency)
        
        # 特征融合
        combined_features = np.concatenate([
            gaze_features, head_features, saliency_features
        ])
        
        # LSTM预测（简化版本）
        self.gaze_history.append(combined_features)
        
        if len(self.gaze_history) == self.seq_len:
            # 使用简化的时序模型预测
            predicted_gaze = self.temporal_predictor(np.array(self.gaze_history))
            return predicted_gaze
        
        return current_gaze  # 历史不足时返回当前值
    
    def temporal_predictor(self, sequence):
        """时序预测器（简化LSTM）"""
        # 简化的注意力机制
        attention_weights = self.compute_attention_weights(sequence)
        
        # 加权平均
        weighted_sequence = sequence * attention_weights[:, np.newaxis]
        predicted = np.mean(weighted_sequence, axis=0)
        
        return predicted[:2]  # 返回预测的(x,y)坐标

class HapticFeedbackOptimizer:
    """触觉反馈优化算法"""
    
    def adaptive_haptic_intensity(self, virtual_object, user_interaction, user_sensitivity):
        """自适应触觉强度调整"""
        # 物体属性
        object_material = virtual_object.get('material', 'default')
        object_rigidity = virtual_object.get('rigidity', 0.5)
        
        # 交互属性
        interaction_force = user_interaction.get('force', 1.0)
        interaction_speed = user_interaction.get('speed', 1.0)
        
        # 用户敏感度
        sensitivity_factor = user_sensitivity.get('haptic', 1.0)
        
        # 基础强度计算
        base_intensity = interaction_force * object_rigidity
        
        # 材料类型调整
        material_multipliers = {
            'metal': 1.2,
            'wood': 0.8,
            'fabric': 0.5,
            'glass': 1.0,
            'default': 1.0
        }
        material_factor = material_multipliers.get(object_material, 1.0)
        
        # 速度相关调整（高速交互减弱细节）
        speed_factor = 1.0 / (1.0 + 0.1 * interaction_speed)
        
        # 最终强度
        final_intensity = base_intensity * material_factor * speed_factor * sensitivity_factor
        
        # 限制在合理范围
        final_intensity = np.clip(final_intensity, 0.1, 1.0)
        
        return final_intensity
    
    def texture_rendering(self, surface_properties, finger_position):
        """表面纹理渲染算法"""
        # 获取表面纹理属性
        texture_type = surface_properties.get('texture_type', 'smooth')
        roughness = surface_properties.get('roughness', 0.5)
        pattern_scale = surface_properties.get('pattern_scale', 1.0)
        
        # 根据纹理类型生成触觉信号
        if texture_type == 'bumpy':
            # 凹凸纹理
            frequency = 10.0 * pattern_scale
            amplitude = 0.3 * roughness
            haptic_signal = amplitude * np.sin(frequency * finger_position)
            
        elif texture_type == 'grainy':
            # 颗粒感纹理
            frequency = 20.0 * pattern_scale
            amplitude = 0.2 * roughness
            haptic_signal = amplitude * np.random.randn(len(finger_position)) * 0.5
            
        elif texture_type == 'ridged':
            # 脊状纹理
            frequency = 5.0 * pattern_scale
            amplitude = 0.4 * roughness
            haptic_signal = amplitude * np.sign(np.sin(frequency * finger_position))
            
        else:  # smooth
            haptic_signal = np.zeros_like(finger_position)
        
        return haptic_signal

class VRPerformanceOptimizer:
    """VR性能优化与渲染算法"""
    
    def foveated_rendering(self, gaze_point, resolution_map):
        """注视点渲染优化"""
        # 创建多层分辨率区域
        foveal_radius = 50  # 像素
        parafoveal_radius = 150
        peripheral_radius = 400
        
        # 分辨率比例
        foveal_quality = 1.0      # 100% 质量
        parafoveal_quality = 0.5   # 50% 质量
        peripheral_quality = 0.25  # 25% 质量
        
        # 为每个像素分配质量级别
        height, width = resolution_map.shape[:2]
        optimized_map = np.zeros((height, width))
        
        for y in range(height):
            for x in range(width):
                distance = np.sqrt((x - gaze_point[0])**2 + (y - gaze_point[1])**2)
                
                if distance <= foveal_radius:
                    quality = foveal_quality
                elif distance <= parafoveal_radius:
                    quality = parafoveal_quality
                elif distance <= peripheral_radius:
                    quality = peripheral_quality
                else:
                    quality = 0.1  # 最低质量
                
                optimized_map[y, x] = quality
        
        return optimized_map
    
    def motion_prediction(self, head_motion_history, prediction_horizon=5):
        """头部运动预测减少运动到光子延迟"""
        # 使用卡尔曼滤波预测未来位置
        n_samples = len(head_motion_history)
        
        if n_samples < 3:
            return head_motion_history[-1] if n_samples > 0 else np.zeros(6)
        
        # 简化的线性外推
        recent_motions = head_motion_history[-3:]
        
        # 计算速度和加速度
        velocity = recent_motions[2] - recent_motions[1]
        acceleration = (recent_motions[2] - recent_motions[1]) - (recent_motions[1] - recent_motions[0])
        
        # 预测未来位置
        predicted = recent_motions[2] + velocity * prediction_horizon + 0.5 * acceleration * prediction_horizon**2
        
        return predicted

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